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Home > Books > Internet of Things (IoT) for Automated and Smart Applications

Smart Home Systems Based on Internet of Things

Submitted: 17 September 2018 Reviewed: 01 February 2019 Published: 28 February 2019

DOI: 10.5772/intechopen.84894

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Internet of Things (IoT) for Automated and Smart Applications

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Smart home systems achieved great popularity in the last decades as they increase the comfort and quality of life. Most smart home systems are controlled by smartphones and microcontrollers. A smartphone application is used to control and monitor home functions using wireless communication techniques. We explore the concept of smart home with the integration of IoT services and cloud computing to it, by embedding intelligence into sensors and actuators, networking of smart things using the corresponding technology, facilitating interactions with smart things using cloud computing for easy access in different locations, increasing computation power, storage space and improving data exchange efficiency. In this chapter we present a composition of three components to build a robust approach of an advanced smart home concept and implementation.

• cloud computing
• event processing
• home appliances
• rule-based event processing

Author Information

Menachem domb *.

• Computer Science Department, Ashkelon Academic College, Ashkelon, Israel

*Address all correspondence to: [email protected]

1. Introduction

Classic smart home, internet of things, cloud computing and rule-based event processing, are the building blocks of our proposed advanced smart home integrated compound. Each component contributes its core attributes and technologies to the proposed composition. IoT contributes the internet connection and remote management of mobile appliances, incorporated with a variety of sensors. Sensors may be attached to home related appliances, such as air-conditioning, lights and other environmental devices. And so, it embeds computer intelligence into home devices to provide ways to measure home conditions and monitor home appliances’ functionality. Cloud computing provides scalable computing power, storage space and applications, for developing, maintaining, running home services, and accessing home devices anywhere at anytime. The rule-based event processing system provides the control and orchestration of the entire advanced smart home composition.

Combining technologies in order to generate a best of breed product, already appear in recent literature in various ways. Christos Stergioua et al. [ 1 ] merge cloud computing and IoT to show how the cloud computing technology improves the functionality of the IoT. Majid Al-Kuwari [ 2 ] focus on embedded IoT for using analyzed data to remotely execute commands of home appliances in a smart home. Trisha Datta et al. [ 3 ] propose a privacy-preserving library to embed traffic shaping in home appliances. Jian Mao et al. [ 4 ] enhance machine learning algorithms to play a role in the security in a smart home ecosystem. Faisal Saeed et al. [ 5 ] propose using sensors to sense and provide in real-time, fire detection with high accuracy.

In this chapter we explain the integration of classic smart home, IoT and cloud computing. Starting by analyzing the basics of smart home, IoT, cloud computing and event processing systems. We discuss their complementarity and synergy, detailing what is currently driving to their integration. We also discuss what is already available in terms of platforms, and projects implementing the smart home, cloud and IoT paradigm. From the connectivity perspective, the added IoT appliances and the cloud, are connected to the internet and in this context also to the home local area network. These connections complement the overall setup to a complete unified and interconnected composition with extended processing power, powerful 3rd party tools, comprehensive applications and an extensive storage space.

In the rest of this chapter we elaborate on each of the four components. In Section 1, we describe the classic smart home, in Section 2, we introduce the internet of things [IoT], in Section 3, we outline cloud computing and in Section 4, we present the event processing module. In Section 5, we describe the composition of an advanced smart home, incorporating these four components. In Section 6, we provide some practical information and relevant selection considerations, for building a practical advanced smart home implementation. In Section 7, we describe our experiment introducing three examples presenting the essence of our integrated proposal. Finally, we identify open issues and future directions in the future of advanced smart home components and applications.

2. Classic smart home overview

Smart home is the residential extension of building automation and involves the control and automation of all its embedded technology. It defines a residence that has appliances, lighting, heating, air conditioning, TVs, computers, entertainment systems, big home appliances such as washers/dryers and refrigerators/freezers, security and camera systems capable of communicating with each other and being controlled remotely by a time schedule, phone, mobile or internet. These systems consist of switches and sensors connected to a central hub controlled by the home resident using wall-mounted terminal or mobile unit connected to internet cloud services.

Smart home provides, security, energy efficiency, low operating costs and convenience. Installation of smart products provide convenience and savings of time, money and energy. Such systems are adaptive and adjustable to meet the ongoing changing needs of the home residents. In most cases its infrastructure is flexible enough to integrate with a wide range of devices from different providers and standards.

The basic architecture enables measuring home conditions, process instrumented data, utilizing microcontroller-enabled sensors for measuring home conditions and actuators for monitoring home embedded devices.

The popularity and penetration of the smart home concept is growing in a good pace, as it became part of the modernization and reduction of cost trends. This is achieved by embedding the capability to maintain a centralized event log, execute machine learning processes to provide main cost elements, saving recommendations and other useful reports.

2.1 Smart home services

2.1.1 measuring home conditions.

A typical smart home is equipped with a set of sensors for measuring home conditions, such as: temperature, humidity, light and proximity. Each sensor is dedicated to capture one or more measurement. Temperature and humidity may be measured by one sensor, other sensors calculate the light ratio for a given area and the distance from it to each object exposed to it. All sensors allow storing the data and visualizing it so that the user can view it anywhere and anytime. To do so, it includes a signal processer, a communication interface and a host on a cloud infrastructure.

2.1.2 Managing home appliances

Creates the cloud service for managing home appliances which will be hosted on a cloud infrastructure. The managing service allows the user, controlling the outputs of smart actuators associated with home appliances, such as such as lamps and fans. Smart actuators are devices, such as valves and switches, which perform actions such as turning things on or off or adjusting an operational system. Actuators provides a variety of functionalities, such as on/off valve service, positioning to percentage open, modulating to control changes on flow conditions, emergency shutdown (ESD). To activate an actuator, a digital write command is issued to the actuator.

2.1.3 Controlling home access

Home access technologies are commonly used for public access doors. A common system uses a database with the identification attributes of authorized people. When a person is approaching the access control system, the person’s identification attributes are collected instantly and compared to the database. If it matches the database data, the access is allowed, otherwise, the access is denied. For a wide distributed institute, we may employ cloud services for centrally collecting persons’ data and processing it. Some use magnetic or proximity identification cards, other use face recognition systems, finger print and RFID.

In an example implementation, an RFID card and an RFID reader have been used. Every authorized person has an RFID card. The person scanned the card via RFID reader located near the door. The scanned ID has been sent via the internet to the cloud system. The system posted the ID to the controlling service which compares the scanned ID against the authorized IDs in the database.

2.2 The main components

Sensors to collect internal and external home data and measure home conditions. These sensors are connected to the home itself and to the attached-to-home devices. These sensors are not internet of things sensors, which are attached to home appliances. The sensors’ data is collected and continually transferred via the local network, to the smart home server.

Processors for performing local and integrated actions. It may also be connected to the cloud for applications requiring extended resources. The sensors’ data is then processed by the local server processes.

A collection of software components wrapped as APIs, allowing external applications execute it, given it follows the pre-defined parameters format. Such an API can process sensors data or manage necessary actions.

Actuators to provision and execute commands in the server or other control devices. It translates the required activity to the command syntax; the device can execute. During processing the received sensors’ data, the task checks if any rule became true. In such case the system may launch a command to the proper device processor.

Database to store the processed data collected from the sensors [and cloud services]. It will also be used for data analysis, data presentation and visualization. The processed data is saved in the attached database for future use.

Smart home paradigm with optional cloud connectivity.

3. Internet of things [IoT] overview

The internet of things (IoT) paradigm refers to devices connected to the internet. Devices are objects such as sensors and actuators, equipped with a telecommunication interface, a processing unit, limited storage and software applications. It enables the integration of objects into the internet, establishing the interaction between people and devices among devices. The key technology of IoT includes radio frequency identification (RFID), sensor technology and intelligence technology. RFID is the foundation and networking core of the construction of IoT. Its processing and communication capabilities along with unique algorithms allows the integration of a variety of elements to operate as an integrated unit but at the same time allow easy addition and removal of components with minimum impact, making IoT robust but flexible to absorb changes in the environment and user preferences. To minimize bandwidth usage, it is using JSON, a lightweight version of XML, for inter components and external messaging.

4. Cloud computing and its contribution to IoT and smart home

Cloud computing is a shared pool of computing resources ready to provide a variety of computing services in different levels, from basic infrastructure to most sophisticated application services, easily allocated and released with minimal efforts or service provider interaction [ 6 , 7 ]. In practice, it manages computing, storage, and communication resources that are shared by multiple users in a virtualized and isolated environment. Figure 2 depicts the overall cloud paradigm.

Cloud computing paradigm.

IoT and smart home can benefit from the wide resources and functionalities of cloud to compensate its limitation in storage, processing, communication, support in pick demand, backup and recovery. For example, cloud can support IoT service management and fulfillment and execute complementary applications using the data produced by it. Smart home can be condensed and focus just on the basic and critical functions and so minimize the local home resources and rely on the cloud capabilities and resources. Smart home and IoT will focus on data collection, basic processing, and transmission to the cloud for further processing. To cope with security challenges, cloud may be private for highly secured data and public for the rest.

IoT, smart home and cloud computing are not just a merge of technologies. But rather, a balance between local and central computing along with optimization of resources consumption. A computing task can be either executed on the IoT and smart home devices or outsourced to the cloud. Where to compute depends on the overhead tradeoffs, data availability, data dependency, amount of data transportation, communications dependency and security considerations. On the one hand, the triple computing model involving the cloud, IoT and smart home, should minimize the entire system cost, usually with more focus on reducing resource consumptions at home. On the other hand, an IoT and smart home computing service model, should improve IoT users to fulfill their demand when using cloud applications and address complex problems arising from the new IoT, smart home and cloud service model.

Some examples of healthcare services provided by cloud and IoT integration: properly managing information, sharing electronic healthcare records enable high-quality medical services, managing healthcare sensor data, makes mobile devices suited for health data delivery, security, privacy, and reliability, by enhancing medical data security and service availability and redundancy and assisted-living services in real-time, and cloud execution of multimedia-based health services.

5. Centralized event processing, a rule-based system

Smart home and IoT are rich with sensors, which generate massive data flows in the form of messages or events. Processing this data is above the capacity of a human being’s capabilities [ 8 , 9 , 10 ]. Hence, event processing systems have been developed and used to respond faster to classified events. In this section, we focus on rule management systems which can sense and evaluate events to respond to changes in values or interrupts. The user can define event-triggered rule and to control the proper delivery of services. A rule is composed of event conditions, event pattern and correlation-related information which can be combined for modeling complex situations. It was implemented in a typical smart home and proved its suitability for a service-oriented system.

The system can process large amounts of events, execute functions to monitor, navigate and optimize processes in real-time. It discovers and analyzes anomalies or exceptions and creates reactive/proactive responses, such as warnings and preventing damage actions. Situations are modeled by a user-friendly modeling interface for event-triggered rules. When required, it breaks them down into simple, understandable elements. The proposed model can be seamlessly integrated into the distributed and service-oriented event processing platform.

The evaluation process is triggered by events delivering the most recent state and information from the relevant environment. The outcome is a decision graph representing the rule. It can break down complex situations to simple conditions, and combine them with each other, composing complex conditions. The output is a response event raised when a rule fires. The fired events may be used as input for other rules for further evaluation. Event patterns are discovered when multiple events occur and match a pre-defined pattern. Due to the graphical model and modular approach for constructing rules, rules can be easily adapted to domain changes. New event conditions or event patterns can be added or removed from the rule model. Rules are executed by event services, which supply the rule engine with events and process the evaluation result. To ensure the availability of suitable processing resources, the system can run in a distributed mode, on multiple machines and facilitate the integration with external systems, as well. The definition of relationships and dependencies among events that are relevant for the rule processing, are performed using sequence sets, generated by the rule engine. The rule engine constructs sequences of events relevant to a specific rule condition to allow associating events by their context data. Rules automatically perform actions in response when stated conditions hold. Actions generate response events, which trigger response activities. Event patterns can match temporal event sequences, allowing the description of home situations where the occurrences of events are relevant. For example, when the door is kept open too long.

The following challenges are known with this model: structure for the processed events and data, configuration of services and adapters for processing steps, including their input and output parameters, interfaces to external systems for sensing data and for responding by executing transactions, structure for the processed events and data, data transformations, data analysis and persistence. It allows to model which events should be processed by the rule service and how the response events should be forwarded to other event services. The process is simple: data is collected and received from adapters which forward events to event services that consume them. Initially the events are enriched to prepare the event data for the rule processing. For example, the response events are sent to a service for sending notifications to a call agent, or to services which transmit event delay notifications and event updates back to the event management system.

5.1 Event processing languages

Event processing is concerned with real-time capturing and managing pre-defined events. It starts from managing the receptors of events right from the event occurrence, even identification, data collection, process association and activation of the response action. To allow rapid and flexible event handling, an event processing language is used, which allows fast configuration of the resources required to handle the expected sequence of activities per event type. It is composed of two modules, ESP and CEP. ESP efficiently handles the event, analyzes it and selects the appropriate occurrence. CEP handles aggregated events. Event languages describe complex event-types applied over the event log.

5.2 Rediscovering workflow from events

In some cases, rules relate to discrepancies in a sequence of events in a workflow. In such cases, it is mandatory to precisely understand the workflow and its associated events. To overcome this, we propose a reverse engineering process to automatically rediscover the workflows from the events log collected over time, assuming these events are ordered, and each event refers to one task being executed for a single case. The rediscovering process can be used to validate workflow sequences by measuring the discrepancies between prescriptive models and actual process executions. The rediscovery process consists of the following three steps: (1) construction of the dependency/frequency table. (2) Induction of dependency/frequency graphs. (3) Generating WF-nets from D/F-graphs.

6. Advanced smart home

In this section, we focus on the integration of smart home, IoT and cloud computing to define a new computing paradigm. We can find in the literature section [ 11 , 12 , 13 , 14 ] surveys and research work on smart home, IoT and cloud computing separately, emphasizing their unique properties, features, technologies, and drawbacks. However, our approach is the opposite. We are looking at the synergy among these three concepts and searching for ways to integrate them into a new comprehensive paradigm, utilizing its common underlying concepts as well as its unique attributes, to allow the execution of new processes, which could not be processed otherwise.

Figure 3 depicts the advanced smart-home main components and their inter-connectivity. On the left block, the smart home environment, we can see the typical devices connected to a local area network [LAN]. This enables the communication among the devices and outside of it. Connected to the LAN is a server and its database. The server controls the devices, logs its activities, provides reports, answers queries and executes the appropriate commands. For more comprehensive or common tasks, the smart home server, transfers data to the cloud and remotely activate tasks in it using APIs, application programming interface processes. In addition, IoT home appliances are connected to the internet and to the LAN, and so expands smart home to include IoT. The connection to the internet allows the end user, resident, to communicate with the smart home to get current information and remotely activate tasks.

Advanced smart home—integrating smart home, IoT and cloud computing.

To demonstrate the benefits of the advanced smart home, we use RSA, a robust asymmetric cryptography algorithm, which generates a public and private key and encrypts/decrypts messages. Using the public key, everyone can encrypt a message, but only these who hold the private key can decrypt the sent message. Generating the keys and encrypting/decrypting messages, involves extensive calculations, which require considerable memory space and processing power. Therefore, it is usually processed on powerful computers built to cope with the required resources. However, due to its limited resources, running RSA in an IoT device is almost impossible, and so, it opens a security gap in the Internet, where attackers may easily utilize. To cope with it, we combine the power of the local smart home processors to compute some RSA calculations and forward more complicated computing tasks to be processed in the cloud. The results will then be transferred back to the IoT sensor to be compiled and assembled together, to generate the RSA encryption/decryption code, and so close the mentioned IoT security gap. This example demonstrates the data flow among the advanced smart home components. Where, each component performs its own stack of operations to generate its unique output. However, in case of complicated and long tasks it will split the task to sub tasks to be executed by more powerful components. Referring to the RSA example, the IoT device initiates the need to generate an encryption key and so, sends a request message to the RSA application, running in the smart home computer. The smart home computer then asks the “prime numbers generation” application running on cloud, to provide p and q prime numbers. Once p and q are accepted, the encryption code is generated. In a later stage, an IoT device issues a request to the smart home computer to encrypt a message, using the recent generated RSA encryption key. The encrypted message is then transferred back to the IoT device for further execution. A similar scenario may be in the opposite direction, when an IoT device gets a message it may request the smart home to decrypt it.

To summarize, the RSA scenarios depict the utilization of the strength of the cloud computing power, the smart home secured computing capabilities and at the end the limited power of the IoT device. It proves that without this automatic cooperation, RSA would not be able to be executed at the IoT level.

A more practical example is where several detached appliances, such as an oven, a slow cooker and a pan on the gas stove top, are active in fulfilling the resident request. The resident is getting an urgent phone call and leaves home immediately, without shutting off the active appliances. In case the relevant IoTs have been tuned to automatically shut down based on a predefined rule, it will be taken care at the IoT level. Otherwise, the smart home realizes the resident has left home [the home door has been opened and then locked, the garage has been opened, the resident’s car left, the main gate was opened and then closed, no one was at home] and will shut down all active devices classified as risk in case of absence. It will send an appropriate message to the mailing list defined for such an occasion.

7. Practical aspects and implementation considerations for IoT and smart home

Smart home has three components: hardware, software and communication protocols. It has a wide variety of applications for the digital consumer. Some of the areas of home automation led IoT enabled connectivity, such as: lighting control, gardening, safety and security, air quality, water-quality monitoring, voice assistants, switches, locks, energy and water meters.

Advanced smart home components include: IoT sensors, gateways, protocols, firmware, cloud computing, databases, middleware and gateways. IoT cloud can be divided into a platform-as-a-service (PaaS) and infrastructure-as-a-service (IaaS). Figure 4 demonstrates the main components of the proposed advanced smart home and the connection and data flow among its components.

Advanced smart home composition.

The smart home application updates the home database in the cloud to allow remote people access it and get the latest status of the home. A typical IoT platform contains: device security and authentication, message brokers and message queuing, device administration, protocols, data collection, visualization, analysis capabilities, integration with other web services, scalability, APIs for real-time information flow and open source libraries. IoT sensors for home automation are known by their sensing capabilities, such as: temperature, lux, water level, air composition, surveillance video cameras, voice/sound, pressure, humidity, accelerometers, infrared, vibrations and ultrasonic. Some of the most commonly used smart home sensors are temperature sensors, most are digital sensors, but some are analog and can be extremely accurate. Lux sensors measure the luminosity. Water level ultrasonic sensors.

Float level sensors offer a more precise measurement capability to IoT developers. Air composition sensors are used by developers to measure specific components in the air: CO monitoring, hydrogen gas levels measuring, nitrogen oxide measure, hazardous gas levels. Most of them have a heating time, which means that it requires a certain time before presenting accurate values. It relies on detecting gas components on a surface only after the surface is heated enough, values start to show up. Video cameras for surveillance and analytics. A range of cameras, with a high-speed connection. Using Raspberry Pi processor is recommended as its camera module is very efficient due to its flex connector, connected directly to the board.

Sound detectors are widely used for monitoring purposes, detecting sounds and acting accordingly. Some can even detect ultra-low levels of noise, and fine tune among various noise levels.

Humidity sensors sense the humidity levels in the air for smart homes. Its accuracy and precision depend on the sensor design and placement. Certain sensors like the DHT22, built for rapid prototyping, will always perform poorly when compared to high-quality sensors like HIH6100. For open spaces, the distribution around the sensor is expected to be uniform requiring fewer corrective actions for the right calibration.

Smart home communication protocols: bluetooth, Wi-Fi, or GSM. Bluetooth smart or low energy wireless protocols with mesh capabilities and data encryption algorithms. Zigbee is mesh networked, low power radio frequency-based protocol for IoT. X10 protocol that utilizes powerline wiring for signaling and control. Insteon, wireless and wireline communication. Z-wave specializes in secured home automation. UPB, uses existing power lines. Thread, a royalty-free protocol for smart home automation. ANT, an ultra-low-power protocol for building low-powered sensors with a mesh distribution capability. The preferred protocols are bluetooth low energy, Z-wave, Zigbee, and thread. Considerations for incorporating a gateway may include: cloud connectivity, supported protocols, customization complexity and prototyping support. Home control is composed of the following: state machine, event bus, service log and timer.

Modularity: enables the bundle concept, runtime dynamics, software components can be managed at runtime, service orientation, manage dependencies among bundles, life cycle layer: controls the life cycle of the bundles, service layers: defines a dynamic model of communication between various modules, actual services: this is the application layer. Security layer: optional, leverages Java 2 security architecture and manages permissions from different modules.

OpenHAB is a framework, combining home automation and IoT gateway for smart homes. Its features: rules engine, logging mechanism and UI abstraction. Automation rules that focus on time, mood, or ambiance, easy configuration, common supported hardware:

Domoticz architecture: very few people know about the architecture of Domoticz, making it extremely difficult to build applications on it without taking unnecessary risks in building the product itself. For example, the entire design of general architecture feels a little weird when you look at the concept of a sensor to control to an actuator. Building advanced applications with Domoticz can be done using OO based languages.

Deployment of blockchain into home networks can easily be done with Raspberry Pi. A blockchain secured layer between devices and gateways can be implemented without a massive revamp of the existing code base. Blockchain is a technology that will play a role in the future to reassure them with revolutionary and new business models like dynamic renting for Airbnb.

8. Smart home and IoT examples

We can find in the literature and practical reports, many implementations of various integrations among part of the main three building blocks, smart home, IoT and cloud computing. For example, refer to [ 12 – 14 ]. In this section we outline three implementations, which clearly demonstrate the need and the benefits of interconnecting or integrating all three components, as illustrated in Figure 5 . Each component is numbered, 1–6. In the left side, we describe for each implementation, the sequence of messages/commands among components, from left to right and from bottom up. Take for example the third implementation, a control task constantly runing at the home server (2) discovers the fact that all residents left home and automatically, initiates actuators to shut down all IoT appliances (3), then it issues messages to the relevant users/residents, updating them about the situation and the applied actions it took (6).

Advanced smart home implementations chart.

The use of (i) in the implementations explanation, corresponds to the circled numbers in Figure 5 .

8.1 Discovery of water leaks and its prevention

First step is deploying water sensors under every reasonable potential leak source and an automated master water valve sensor for the whole house, which now means the house is considered as an IoT.

In case the water sensor detects a leak of water (3), it sends an event to the hub (2), which triggers the “turn valve off” application. The home control application then sends a “turn off” command to all IoT (3) appliances defined as sensitive to water stopping and then sends the “turn off” command to the main water valve (1). An update message is sent via the messaging system to these appearing in the notification list (6). This setup helps defending against scenarios where the source of the water is from the house plumbing. The underlying configuration assumes an integration via messages and commands between the smart home and the IoT control system. It demonstrates the dependency and the resulting benefits of combining smart home and IoT.

8.2 Smoke detectors

Most houses already have the typical collection of smoke detectors (1), but there is no bridge to send data from the sensor to a smart home hub. Connecting these sensors to a smart home app (2), enables a comprehensive smoke detection system. It is further expanded to notify the elevator sensor to block the use of it due to fire condition (1), and so, it is even further expanded to any IoT sensor (3), who may be activated due to the detected smoke alert.

In [ 5 ] they designed a wireless sensor network for early detection of house fires. They simulated a fire in a smart home using the fire dynamics simulator and a language program. The simulation results showed that the system detects fire early.

8.3 Incident management to control home appliances

Consider the scenario where you leave home while some of the appliances are still on. In case your absence is long enough, some of the appliances may over heat and are about to blowout. To avoid such situations, we connect all IoT appliances’ sensors to the home application (2), so that when all leave home it will automatically adjust all the appliances’ sensors accordingly (3), to avoid damages. Note that the indication of an empty home is generated by the Smart Home application, while the “on” indication of the appliance, is generated by IoT. Hence, this scenario is possible due to the integration between smart home and IoT systems.

9. Conclusions and summary

In this chapter we described the integration of three loosely coupled components, smart home, Iot, and cloud computing. To orchestrate and timely manage the vast data flow in an efficient and balanced way, utilizing the strengths of each component we propose a centralized real time event processing application.

We describe the advantages and benefits of each standalone component and its possible complements, which may be achieved by integrating it with the other components providing new benefits raised from the whole compound system. Since these components are still at its development stage, the integration among them may change and provide a robust paradigm that generates a new generation of infrastructure and applications.

As we follow-up on the progress of each component and its corresponding impact on the integrated compound, we will constantly consider additional components to be added, resulting with new service models and applications.

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Smart Home: Everything you need to know

Rapid technological progress, increasing digitization and ever larger data streams: The idea of a connected “smart” home is more topical and enticing than ever. But just what does “smart home” mean? Where do the roots of this technology lie? What are the benefits and risks? What will the smart home of the future look like? We look at the mega trend from various perspectives.

Everyday life made smart

Mr. Watson is late again. He has no time for breakfast. But at least he has the basic necessities: Bang on the minute, the coffee machine supplies his first hot drink, with the defined quantity of milk and froth. Mr. Watson quickly gets ready for a stressful day at the office. The motion sensor is activated and notifies the garage door, which now opens. In all the rush, he has forgotten to switch off the iron and light. No problem, he deals with that while underway via his smartphone. At the office, Mr. Watson activates the washing machine so that the washing is done just in time for when he knocks off work. Have the kids got back from school safely? A glance at the recordings made by the indoor camera in the entrance hall reassures dad.

Shortly before arriving home, Mr. Watson activates the robovac and the “after-work” scenario via smartphone. While the vacuum cleaner does its work, the living room is bathed in a cozy light. The heating ensures a pleasant, predefined temperature, while soft jazz music makes for a relaxed atmosphere. The shutters close later in the evening. That’s the signal for the outdoor camera in the garden to step into action and monitor the patio and garden.

Definition: What is smart home?

“Smart home” denotes the use of technical systems, automated processes and connected, remote-controlled devices in apartments and houses. The main objective of the functions is to improve the quality of life and convenience in the home. Other goals are greater security and more efficient use of energy thanks to connected, remote-controllable devices.

Home appliances , such as the washing machine, lights or the coffee maker, can be time-controlled. Devices like motion sensors, cameras, shutters or thermostats initiate user-programmed processes. The heart of the smart home is the central control unit, with which various smart components are connected and can be controlled from the PC, smartphone or tablet. Common wireless standards such as Wi-Fi, Bluetooth, ZigBee or Z-Wave are used for communication or controlling devices. The central control unit is also termed a hub or gateway.

Back to the future: History of smart home

It’s human nature to find ways that make everyday life easier and more pleasant. The area of “ home automation ” – in effect the predecessor of the smart home – was brought to life through technological progress, in particular through the Internet and computer. Science fiction literature in the 1950s portrayed the first visions of homes that are monitored and controlled fully automatically by machines. The 1999 Disney film “Smart House” was about household computers and the consequences when smart machines take on a life of their own. And Disney proved to be unintentionally visionary in the part of the movie where the house’s intelligent control unit develops the feeling of jealousy. In reality, it will likely be a few years before machines can “generate” emotions – fortunately.

Scientists have already been working for more than 30 years on connecting home appliances and automating their use. Yet it’s only been in the past 15 years that the issue of the smart home has aroused broad public interest. The main reasons: Current challenges as a result of trends like an aging society, greater environmental awareness and the related wish for a sustainable energy supply. Increasing digitization and new means of enhancing convenience in our own four walls were further factors that put the smart home at the center of public interest at the turn of the millennium.

The Fraunhofer inHaus Center, which was opened in Duisburg in 2001, is a lighthouse project in German-speaking countries. The project involves exploring and testing new system solutions and products from the smart home segment in a residential environment. “The House of the Present” in Munich showcased a connected home with centrally controlled electronic processes from 2005 to 2011. The first T-Com House from Deutsche Telekom in Berlin was opened to interested visitors in 2005. The focus of this model project was on connecting various home appliances and controlling them by means of different input devices.

Technological teamwork

For decades now, a wide range of different home appliances have helped make everyday life more pleasant, speed up processes and hence save time and work. So what additional benefits does the smart home deliver? Without the smart home, the impetus for a machine’s every action has to come from humans, who start processes manually and activate each device individually at the right time. The smart home relieves them of this work by enabling components to communicate with each other. Devices start, control and monitor specific processes in the home on their own, depending on the scenario and on the basis of how they are programmed. Interoperability is the magic word. If devices are interoperable, they can communicate with each other. Only then does the alarm system activate itself when the shutters are being closed. Only then does the heating switch itself off when the window is opened. If there is no interoperability between the elements, the home is simply not smart.

Apart from enhanced convenience, better energy efficiency and greater security are other key aspects. If a smart home thermostat communicates with the window contact via WiFi, it detects a window being opened and thus regulates the temperature. Such a thermostat switches the heating off as soon as it receives information that no one is at home any more from the sensors of other devices. Smart LED lights automatically emit different tones of color depending on the time of day and room. If the outdoor camera on the patio is activated as a result of movements on the property, it also puts the indoor camera on alert, since there might be the threat of a burglary. In homes with elderly inhabitants, a pressure-sensitive mat could notify relatives whether someone has got out of their bed as usual in the morning.

The way to a smart home of our own

How is a home transformed into a smart home? The required components can be installed and configured without any technical know-how. The following aspects should be taken into account in planning:

• An Internet connection and WiFi are required
• A smartphone or tablet are best suited for controlling and monitoring the devices
• A wireless network is modern, convenient and elegant, but transmission by cable is more secure
• Should the devices be programmable from the central control unit and interoperable, or is a standalone solution enough?
• Are all the devices connected using the same wireless standard (e.g. WiFi)?
• Starter sets are ideal for beginners, but usually only cover a single area: either energy efficiency, security or convenience
• The central control unit should be placed so that all the devices to be addressed are within its radius

My smart home is my castle – security and data protection

While many technologies deliver benefits, they also entail risks that users should be aware of and minimize. In the case of the smart home, connecting devices and communication by them via wireless connections such as WiFi harbor certain risks. First, personal data may be able to be misused (camera recordings, photos, etc.). Second, there is the risk of cyber criminals manipulating individual smart home components. As part of the latest Cyber Security Insights Report by the U.S. software company Symantec, more than 20,000 smart home users worldwide were surveyed, including 1,000 from Germany. The results show how people sometimes make it easy for online criminals to access data and devices:

The most common problems with securing the smart home

• 10 percent of German users don’t protect their home WiFi with a password
• Every one in ten doesn’t change the preset default password, 35 percent of users worldwide have at least one unprotected device, which makes the household prone to attack by online criminals
• 45 percent of those surveyed don’t know how to secure their WiFi or router
• 60 percent say they are not able to update the firmware

Yet those surveyed are aware of how important it is to protect personal data on the Internet, which also includes protecting the personal data of others. For instance, outside cameras should only film the owner’s property. There are models that allow the user to define the areas that are filmed. Although part of the neighboring property is captured on camera, these areas are grayed out or pixelated. In exceptional cases, public authorities are even allowed to use smart home user data for prosecuting crimes. However, given the current legal position, that’s only imaginable in the case of serious crimes, such as homicide. If the smart home is a rented apartment, access to the data of the window contacts and heating may be conceivable in order to examine whether the tenant has caused mold to grow through incorrect ventilation or there is a building defect.

10 tips for greater security

Just a few steps and rethinking old habits can increase security in the smart home significantly.

• Do not access personal information or social media accounts in unsecured wireless networks
• Install all available updates to security software as soon as possible
• Use the strong encryption method WPA2 for your WiFi
• Disable additional functions, such as a camera or microphone, on devices if you don’t absolutely need them
• Use strong passwords, ideally comprising ten upper-case and lower-case letters, symbols and digits
• Do not open attachments in e-mails from unknown senders without checking them
• Switch off connected devices when you don’t need them
• Change the default login data and passwords stored in the factory settings of the devices
• Switch from WiFi to cable connections where possible
• Use a central control unit with encrypted data transmission and local data storage

Smart future

The German smart home market will triple in volume to €4.3 billion by 2022, according to the study “The German Smart Home Market 2017-2022. Facts and Figures.” The annual growth rate in the coming five years will thus average 26.4 percent. By comparison: Traditional industries, such as mechanical engineering, have an average maximum growth rate of six percent per annum. The rapid pace of technological progress means many innovative smart home elements will be used in just a few years’ time. IKEA is working on a smart table that detects food with a camera and suggests recipes based on the ingredients. That reduces waste and enables existing food to be used better. In the future, smart mirrors will analyze our skin and recommend care products on that basis.

The popular vision in pop culture of the traditional robot as a home help will become reality in the coming years. The robot does the washing, serves food and drinks, and also provides the inhabitants with useful information on the side. We can follow the baking or cooking process live in the smart oven of the future thanks to a built-in camera. The toilet of the year 2030 will provide users with information on the state of their health and can even conduct pregnancy tests.

The next big evolutionary advance to cater for people’s needs for ecological sustainability and a better quality of life is the concept of the  smart city : Residents will commute to the office in autonomous  electric cars  or on connected  e-bikes . Parcels will be delivered by  drones . People will travel at high speed, without emitting pollutants, in the  hyperloop , a sort of gigantic vacuum tube system in which passengers or goods are conveyed in an airless tube by means of magnetic levitation technology. According to the tech visionary and Tesla CEO Elon Musk, the 570 km trip from San Francisco to Los Angeles will then take just 35 minutes. That means more time for old-fashioned things: a pleasant talk with the passenger, a recuperative midday nap on the way to the next meeting, or a good book made of real paper.

The most important questions and answers at a glance

The garage door opens as soon as you cross the doorstep, you can also switch off the lighting while you are out and about, and the outdoor camera helps ensure you are safe in your own four walls at night: “Smart home” denotes the use of technical systems, automated processes and connected, remote-controlled devices in apartments and houses. Its objective is to improve the quality of life and convenience in the home, as well as residents’ safety and security. Smart home applications also often ensure more efficient use of energy.

There are now a large number of different smart home applications that are all intended to make everyday life easier for us. If, for example, you are out and realize you have left the light on, you can switch it off very easily using your smartphone. Conversely, you can set the heating to the desired temperature or turn on the washing machine before you arrive home. Appliances like a robovac can also be controlled conveniently from your smartphone or tablet. Many components initiate user-programmed processes: If, for example, the shutters are closed, the outdoor camera and motion sensor are activated. Other home appliances, such as lights or the coffee maker, are instead time-controlled, in other words, are switched on and off automatically at defined times.

Smart home devices start, control and monitor specific processes in the home on their own, depending on the scenario and on the basis of how they are programmed. In particular, they relieve people of work if they communicate with each other: Only then does the heating switch itself off as soon as the windows are opened, for example. The heart of this system is a central control unit – also called a hub or gateway – via which the various components are connected with each other. Common wireless standards such as Wi-Fi, Bluetooth, ZigBee or Z-Wave are used for communication and controlling the technology.

What is probably one of the most crucial advantages of a smart home: Users are relieved of a lot of work since they no longer need to start individual devices and processes manually at the right time. At the same time, devices such as motion sensors or cameras ensure greater security by enabling your home to be monitored, even if you are on vacation, for instance. Thermostats or smart lighting also help save energy: The heating switches itself off as soon as a window is opened, for example. An advantage of all that is also that the smart home is relatively easy to set up – even without any technical know-how.

A question many people ask about the smart home is: Just how secure are the applications in my home? Indeed, there is the possibility that personal data may be misused, for example when a camera makes recordings. There is also the risk of hackers gaining access to smart home components and manipulating them. So how can the system’s security be increased? That can be done with just a few steps: Security software should be used and updated regularly, for example. Many users also do not use a password, or only use a weak one, for their network – a problem that can soon be remedied. In addition, individual device functions, such as microphones, can be deactivated if they are not needed.

One thing is very clear: the trend is toward the smart home. The German smart home market alone is expected to triple in volume to €4.3 billion by 2022. The rapid pace of technological progress means many new components will also enter the home in the future. The main focus of that is domestic robots. The next big step toward the future is the smart city, where not only households, but also cars, the infrastructure or drones will be connected with each other.

Last update: November 2017

Further topics

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The Ultimate Guide to Setting Up Your Smart Home

The Smart Home: It's a place that anticipates your needs and empowers you to fine-tune your environment. Well, that's the pitch at least. Putting it all together isn't a smooth ride, but the right setup and combination of devices can make your life easier and add real convenience.

With a myriad of ecosystems and standards to navigate, not to mention the diverse array of devices, the smart-home scene is daunting. We put together this smart-home guide to highlight your options, explain the jargon, and help you understand the consequences of the choices you make. A little planning goes a long way. 

Updated February 2023: We added alternative ecosystems, updated the Wi-Fi and smart-home standards sections, and added some relevant links.

If you buy something using links in our stories, we may earn a commission. This helps support our journalism. Learn more .

• Pick Your Ecosystem
• You'll Want a Hub
• The Importance of Wi-Fi
• Bluetooth, Wi-Fi, or Smart Hubs?
• Verify Smart-Home Support
• Understanding Smart-Home Standards
• Setting Up Smart-Home Devices
• Find Good Spots and Pick Names Carefully
• Grouping, Automation, and Routines
• What to Do When You Move or Change Routers
• A Word on Security
• Troubleshooting Tips

Google's Nest Mini is the cheapest way to enter its ecosystem of smart-home devices, or you can just use your Android phone.

Before you start shopping for devices, decide which ecosystem works best for you. There are three main ones: Google Home, Amazon Alexa, and Apple HomeKit. If your home is filled with iPhones, iPads, and Macs, the latter is the obvious choice, but if you have an Android phone, you may prefer Google's Home platform. Third-party devices usually offer support for multiple standards, but things will run more smoothly if you pick one main ecosystem. Here's a quick breakdown of each:

Google Home: Google Assistant, the voice assistant, is the main strength of the Nest ecosystem . It swiftly responds to voice commands, is smart enough for a conversational style of speaking , and understands complicated commands or follow-up requests that will confound Alexa or Siri. If you have an Android device, Google Assistant is baked in, and the Google Home app offers up quick access to smart-home shortcuts. 

Amazon Alexa: With a head start in the smart-home arena, Amazon’s Alexa boasts the widest range of compatible products. You can ask it anything, though its answers aren't always as accurate as Google's. Alexa supports a wide choice of Skills (like smartphone apps) that have been developed by third parties, and its speakers and smart displays are the most affordable, especially if you wait for big sale events like Prime Day. If you want to control Alexa from your phone, you need to install the Alexa app, and it must be open before you can issue a voice command. 

Apple HomeKit: HomeKit is the most restrictive of the three, but it's still the best option for iPhone owners. Apple’s tighter control over third-party certification ensures smooth operation for supported devices. You won't find as many HomeKit-supported devices as with Alexa or Google Assistant, but the major smart-home brands are covered. Apple's Home app is elegant and easy to use, devices are easy to set up, and its platform is the most secure. Apple collects less data by default, and data is kept on the device whenever possible. If you want to control devices when you’re away from home, you need a HomeKit hub device, such as a HomePod Mini, Apple TV, or iPad. Siri is also the weakest of the three voice assistants, though it's getting better .

Eric Ravenscraft

Adrienne So

Chris Haslam

Google, Amazon, and even Apple, to a lesser degree, collect data on your usage habits. That includes voice recordings of your interactions with their assistants. These are reviewed by humans for accuracy, but backlash over transparency has led to better ways for you to control exactly how your data is handled. We have several guides on how you can keep your recordings private with all three of these platforms, how to delete stored voice recordings and activity , and how to make these smart speakers and smart displays as private as possible .

Other options : There are a few other ecosystems you might consider if you want to avoid the big three. Home Assistant is an open-source platform that's designed to give you local control and safeguard your privacy. It is a powerful platform with an active community and offers many integrations and automation options, but it is also complex and time-consuming to understand and configure. 

You could also try Samsung's SmartThings or Tuya's Smart Life, although both platforms use Google Assistant or Amazon Alexa for voice commands. 

Amazon's Echo Show is a smart display. These hubs are especially great for controlling your smart-home devices if you have guests that aren't used to voice controls or don't have the right apps installed. 

You can control all your smart-home gadgets with your smartphone, whether it's by talking to a respective assistant or using an app. However, we recommend having a mix of smart speakers and smart displays throughout the home so your assistant can always hear you (you won't need to shout commands too loudly). These are our favorite smart speakers and smart displays . The latter are more versatile, as they offer simple touch controls anyone can use. It’s important to consider other people you live with and guests who may be unfamiliar with smart-home setups.

Take smart lighting, for example. You have to leave light switches in the on position if you want to control them with a voice assistant. But without a physical switch, you might confuse guests. Children also may not have access to controls through a phone or be comfortable with voice controls. People are likely to turn your switches off; years of muscle memory make it a tough habit to break. You can get around this by buying smart switches, but think carefully about whether you want to replace your original switches or have a second set side-by-side.

Most ecosystems now have a way for anyone in the home to create their own profile, and some assistants can even identify who in the household is speaking, for a more tailored experience. Whatever solution you decide on, you should demonstrate how to use it, so that your family, roommates, and guests are comfortable with how it works.

Almost all smart-home devices require a reliable Wi-Fi connection. What you need to know are the two most-used frequencies: 2.4 GHz and 5 GHz. Most smart-home devices operate on the 2.4-GHz frequency, though that’s starting to change. It has a longer range, but the 5-GHz frequency offers faster speeds. 

A relatively new Wi-Fi protocol, named Wi-Fi 6E , supports 6-GHz, which is potentially even faster ( Wi-Fi 7 will also use the 6 GHz band). Wi-Fi 6E can handle more devices, uses less power, and is more secure, but all of your gadgets need to support Wi-Fi 6E, including your mesh system or router, and it's even shorter range than 5 GHz. While more Wi-Fi 6E devices are hitting the market, it's mainly something you'll want to consider for future-proofing right now.

Congestion, where Wi-Fi signals interfere with each other, can be an issue, particularly for people living in apartments. You can use an app to check how busy your Wi-Fi channels are and potentially switch to a different channel, though most routers should handle this automatically. Another consideration is router limitations. Most modern routers support up to 250 devices in theory, but performance can suffer long before you reach the limit.

Make sure that you choose a good spot for your router , and remember that there are many ways to make your Wi-Fi faster . If you have a basic router supplied by your internet service provider (ISP) or an older model, upgrading to a new router could bring major benefits. Larger properties or homes with Wi-Fi dead spots may benefit from a mesh system.

To keep things secure, it is best to choose a long password with a mix of lowercase, uppercase, numbers, and special characters for your Wi-Fi. Consider connecting smart home devices to a separate network (some router manufacturers have an IoT network option) and always set up a Guest network for visitors to use (this is a standard router option now).

Certain smart-home devices offer the option to connect via Bluetooth, Wi-Fi, or a special smart hub, like Philips Hue bulbs. Bluetooth is far slower and less reliable than Wi-Fi, and while Wi-Fi might seem like the easiest solution, a dedicated smart hub can help reduce congestion, offer more stability, and make connected devices more responsive. 

Hubs generally use a different technology to connect devices, something that is low power, low bandwidth, and long range. Thread, Zigbee, LoRa, Z-Wave…the list of technologies goes on and on. While some hubs are special standalone devices, it's growing more common for manufacturers to integrate technologies like Thread into smart speakers, displays, routers, and other devices. The trade-off is that hubs need power and sometimes require a free Ethernet port in your router to plug into.

Some devices, like the Sonos Beam, have built-in Google Assistant and Alexa, meaning you don't need a separate smart speaker.  

To see if a smart-home product works with your ecosystem of choice, look for a logo on the box or webpage. At a minimum, you want to see one of these:

• Works with the Google Assistant
• Works with Alexa
• Works with Apple HomeKit

These logos ensure a basic level of support. It means you can connect it to the respective ecosystem and control the gadget with your voice. That said, support for an ecosystem doesn't mean the same thing for each product. One robot vacuum might simply have start and stop voice command support, while another can be told to clean a specific room or work until a certain time. Always check the full list of commands or user reviews to get a complete picture of what’s possible.

You'll also find third-party smart-home devices with built-in voice assistants. There’s a separate “Alexa built in” logo that means you can talk directly to Alexa through the device. The Google equivalent is simply a “Google Assistant” logo. The Sonos Beam soundbar is an example of a device that has both Google Assistant and Alexa inside, so you can talk directly to it like you would to a Nest or Echo speaker. Siri is only available in Apple-made devices, but it will soon come to third-party devices.

A lack of common standards has hindered the smart-home scene for years. Things are beginning to change, but it’s still confusing. Different wireless standards connect your smart-home devices behind the scenes. Two popular examples are ZigBee (used by Philips Hue, Logitech, LG, and Samsung) and Z-Wave (used by Honeywell, GE, and also Samsung). 

Thread is a newer standard (used by Apple, Google, and Nanoleaf) that creates a mesh network without the need for a hub. Then there’s Bluetooth and Bluetooth LE (low energy). This is by no means an exhaustive list, as there are many other standards out there.

For the most part, these behind-the-scenes technologies don’t matter, as you can use a mix of them in your home. It's up to device manufacturers to choose which of the three main ecosystems they want to support (if not all), regardless of the underlying technology.

But that's where Matter comes in . It's a relatively new wireless interoperability standard. The aim is to have all smart-home devices work together securely, reliably, and seamlessly. More than 200 companies are on board, including Google, Amazon, Apple, Samsung, and the ZigBee Alliance. Matter acts as a middleman cutting across standards and ecosystems to make setup simple and enable everything to play nicely together. With Matter, a Google Nest Hub smart display can show the video feed from a Ring doorbell, for example. (They currently don't play nicely.)

While most new devices will likely support Matter, many older devices will also be updated to support the new standard. In smart lighting, both Philips Hue and Nanoleaf have confirmed that current and future devices will support the standard. Google also says Matter support is coming to Nest devices and Android phones, to make the setup of any Matter device a breeze through the Google Home app.

Big smart-home brands offer easy compatibility with the major ecosystems. Philips Hue bulbs, for example, can be added directly from the Google Home or Apple Home apps. Unfortunately, this isn't common. Most devices will require you to use a third-party app for the initial setup at the very least, and possibly also for configuration and control.

The setup guide that comes with every smart-home device generally directs you to download the companion app as the first step. You may have to scan a QR code or enter a serial number, so make sure you run through this process before you mount anything in place or throw anything away, as these codes often appear on the back or underside of devices or the instruction booklet.

Linking to your chosen ecosystem might be a part of the setup process, but that's also not always the case. You may have to dig into your Apple Home or Google Home settings to manually link your account. With Alexa, you will likely have to install the relevant skill.

After setting up a device and linking it to your chosen ecosystem, you may not have to use the third-party app again, but this varies depending on the gadget. The Google Home, Apple Home, and Alexa apps tend to offer a simplified set of controls for most devices, so it might be good to keep the third-party app around just in case you need to access particular settings.

For any smart-home device, it’s important to find a suitable spot. They will likely need a power outlet and a decent Wi-Fi signal. You should also consider accessibility, especially if it’s a battery-powered device that’s going to require periodic charging. Never physically install anything like a security camera without testing the device in that spot first.

You may be tempted to choose silly names for your smart-home devices or not give it much thought, but it’s important to stick with a consistent naming convention. Voice commands have to be precise or they won’t work. We recommend naming devices according to the room they’re in, so you have “living room speaker” and “office light,” for example. When you have multiple devices in a single room, as you probably will with lights, you can number them or go by area. 

Whether you go for “kitchen light counter” and “kitchen table light”—or “kitchen light 1” and “kitchen light 2”—doesn’t matter, so long as everyone is clear on what the names are. It’s crucial to watch out for duplicate names, as they can cause issues for smart-home platforms and voice assistants.

You can schedule devices like robot vacuums to start at specific times of day, so you always have a clean home.

You can group devices or organize them into rooms in your chosen smart-home ecosystem's app. This is a vital step when you have multiple lights, for example, because you don’t want to be saying “Turn on living room light 1,” then “Turn on living room light 2.” If you organize all the lights into a group, you can simply say “Turn on living room lights.”

Depending on the platform you’re using, you can group rooms, create subsets of devices within rooms, or group devices across multiple rooms by creating custom groups, zones, or rooms. It’s worth taking some time to think about this, as it gives you much greater control and flexibility for voice commands and for setting up automation or routines.

One of the best parts about the smart home is that you can automate it. For example, your smart-home system might detect when you leave the house and turn everything off, or react to you coming home by turning on lights, cranking up the air conditioner, and playing your favorite playlist. 

You can schedule various actions, too. This might be handy for certain devices like robot vacuums to keep your house well maintained. Some tasks can be triggered with a specific, customizable word. Just by saying “Good morning” to Google Assistant, you can set it up to deliver a weather and news report, have it open your curtains, and turn on the coffee machine. Our guides on creating Google Home routines , Alexa routines , and setting up Siri Shortcuts will help you get started. 

If you have a range of devices or services that span different platforms, you can still create automated routines (or applets) if they support a third-party service called IFTTT (If This Then That). Routines with Google, Alexa, and Siri are limited in scope, but IFTTT offers complex chains of automation and links together disparate devices and software. You can have IFTTT turn on your Philips Hue front garden and porch lights when your video doorbell sees someone approaching, for example.

The prospect of having to set up every device again after moving to a new house or changing your router can be dispiriting, especially if you don’t have the box or instructions for every device on hand. You can take a lot of the pain out of this process by simply setting the same router name and password as before. 

If someone is moving out of your home, read our guide on how to un-set up your smart home so they can't remotely control your gadgets.

Smart-home devices might have microphones and cameras inside them, which can have implications for your privacy. There's always a risk of security breaches, which can expose personal information or recordings stored in the cloud. Always read the manufacturer’s privacy policy and make sure you’re comfortable with how they use your data. It’s also a good idea to do some research on smart-home device manufacturers to see if they've been a part of any recent hacks or issues.

Most smart speakers and smart displays have physical mute buttons, but you might forget to unmute them, which can also be a hassle if you're not nearby. One option is to consider plugging your speaker or display into a smart plug . That way, you can schedule times to turn them off when they're least used. 

Think very carefully before you buy a smart-home device with a camera inside, whether it's a robot vacuum or a smart display. Most aren't necessary unless you're buying a security camera . That said, if you have a smart display with a camera for video calls, you can buy webcam covers that can slide open when you need the camera.

Try to use devices that support multi-factor authentication (commonly called two-factor authentication ) to reduce the risk of someone gaining access with stolen credentials. It'll require you to confirm your account with a text message or email (the latter is more secure). Some devices allow for biometric login using a fingerprint or facial scan on your phone, which is secure and convenient. These features are not always on by default, so take the time to set them up.

Check out our digital security guide and our guide to personal data collection for more tips on keeping your digital life secure.

No matter how closely you follow instructions, things can—and frequently do—go wrong when you’re setting up smart-home devices. Here are a few tips we’ve learned the hard way.

• Check that your phone is connected not just to the same network, but the same Wi-Fi band as the device you’re setting up (most devices need 2.4 GHz). Many routers choose the band automatically, so we have a guide on how to set up smart home devices with 2.4 GHz Wi-Fi .
• If setup for a device doesn't work, repeat the process. Some devices fail or hang indefinitely at first, but if you quit and load the app again, they often connect straight away.
• Turn things off and on again. It works surprisingly often and applies to smart-home devices too.
• If you’ve already rebooted the smart-home device, try rebooting your router. You should also check that the router has the latest firmware.
• Search online and be specific about the model of the device and the issue you’re having. Hit the support forums and see if you can find a thread discussing your issue. If you’re lucky, someone will have suggested a workaround or fix.
• A factory reset is your final option. Check the instructions for how to return the device to factory conditions, and consider also deleting and reinstalling any companion app so you can try a fresh setup from scratch.

Now that you have a good grasp of how the smart home works, check out our guides to the best smart light bulbs , smart lighting panels and strips , smart speakers , smart plugs , outdoor and indoor security cameras , smart displays , and robot vacuums .

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• Smart Home /
• Amazon Alexa
• How to start a smart home using Amazon Alexa

From its expansive ecosystem to the dreaded ‘by the way...’ pitch, here’s everything you should know before Amazon Alexa controls your smart home.

By Jess Weatherbed , a news writer focused on creative industries, computing, and internet culture. Jess started her career at TechRadar, covering news and hardware reviews.

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During Amazon’s Prime Day sale four years ago, I found myself staring at a listing for an Echo Dot smart speaker. It was on sale for half price. I wasn’t browsing with any particular smart home gadgets in mind. It was simply being advertised on the homepage of the e-commerce giant’s website, and I lack the impulse control to resist a good bargain. I purchased it thinking I’d just use it as a fancy voice-controlled radio for my kitchen — much like my parents’ second-gen Echo back home — and told myself that it was the last time I’d line Jeff Bezos’ wallet with my hard-earned cash.

Fast forward to 2023, and Alexa is now on its way to running my entire apartment.

Its voice rings through almost every room. It manages my shopping list, listens for when my laundry needs switching over, nags me when it’s time for bed, and even turns on my towel warmer when I go for a shower. I never intended for this to spiral into a full smart home setup, but the convenience it has provided me with is worth every penny I spent on peppering Echo smart speakers and Alexa-compatible plugs and lighting around the place.

When I mention Alexa here, I’m specifically referring to the Alexa Voice Service (AVS), which is essentially a cloud-based service that can mimic conversations and perform tasks designated with vocal commands. Amazon describes it as the company’s “voice AI” — when it detects someone saying its wake word (usually “Alexa,” but more on that later), the service will start listening and responding to your demands. You can instruct it to play music via radio stations or streaming services like Amazon Music and Spotify , ask it what the weather will be like, and set alarms and timers to keep you on track throughout the day. Routines can also be created to group together various actions, such as switching off all your smart lighting when you say, “Cut the lights.”

Amazon spent at least four years designing what would eventually become the first Echo smart speaker before it was officially announced in November 2014. The Alexa virtual assistant — named after the library of Alexandria — launched alongside it, with its now-iconic voice born from Amazon’s acquisition of a Polish speech synthesizer called Ivona. Unlike rival voice assistants such as Apple’s Siri and Microsoft’s Cortana, Alexa quickly defined itself by actually being useful . It didn’t need a phone or Windows 10 to work, and the routines and skills built into the system allowed it to perform tasks like hosting trivia games, controlling your TV using voice commands, and fetching stock or weather reports from specific third-party services.

A lot has changed in seven years. These days, Alexa can be used via a mobile app and third-party devices like TVs and speakers, kicking off a trend in interactive voice assistants and fully embracing its calling as a robust smart home platform. It sits beside Google Home, Apple Home, and Samsung SmartThings as one of the four main ecosystems for smart home devices. Google Home, Apple Home, and SmartThings are solid options, but there are a few reasons why I’ve remained faithful to the Amazon Alexa platform.

Spoiled for choice

For one, Amazon provides the largest selection of smart speaker devices and frequently releases new models with generational improvements. Amazon Echo devices can be used as your smart home controller, and these days, there’s an Echo for everyone: you can get the fourth-generation Echo for $99 , the more premium Echo Studio for $200 (which features Dolby Atmos and spatial audio technology), the compact Echo Dot for $50 , and the affordable new Echo Pop for $40 . Variants of the Echo Dot are then available that feature a built-in LED clock ($60) or colorful designs specifically created for children that come with a free 12-month membership to Amazon Kids Plus ($60), an all-in-one subscription service that provides access to books, audiobooks, educational apps, and games.

The Echo Show smart display also comes in several varieties, ranging from the adorable $90 Echo Show 5 to the $280 15.6-inch Echo Show 15 . And all that doesn’t even include compatible Echo accessories like the Echo Sub ($130) or Echo Link Amp ($300) . That variety means that you can select a smart home controller that best serves your needs, and there are plenty of deals to be found on older, still capable models if you’re on a tight budget. Apple’s and Google’s offerings can be similarly affordable, but they have far fewer smart speaker offerings by comparison.

I personally have two fifth-gen Echo Dot speakers in my lounge and home office, a fifth-gen Echo Dot with Clock for the bedroom, and a second-gen Echo Show 5 in my kitchen. The illuminated clockface on the bedroom-based Echo Dot is an ideal modern alarm clock, and I can use the Echo Show to view the stream from a security camera or video doorbell , display recipes, or video call friends while I’m cooking. These Echo devices were basically the gateway drug that got me expanding into other smart home integrations. They’re simple and incredibly user-friendly, which makes them a good way to ease yourself into the Amazon ecosystem before fully committing to buying expensive add-ons — you can play around with Alexa’s various skills and routines until you’re comfortable with adding in additional hardware.

And there are plenty of skills to play around with — over 100,000, at this point, far more than Google Home Actions and Apple’s user-programmable Shortcuts, each platform’s equivalent feature. Skills are essentially optional preprogrammed apps for your Alexa device that perform specific tasks or fetch information from a particular source. For example, you can install skills that allow you to order from Pizza Hut using your voice or get detailed weather reports from Big Sky , which uses the Dark Sky API. Plus, there are lots of goofier skills you can install to play games, tell jokes, and change how Alexa interacts with you. Most skills are free, though some do require a paid subscription to unlock all of the features, like Big Sky’s $1.60 monthly premium membership.

Hunches and customizable wake words

You’ll find that each of the three big smart home systems can largely perform the same tasks, but Amazon Alexa has at least two perks I can think of that its rivals lack. The first is Hunches , which allows Alexa to proactively control your smart home gadgets based on your previous activity. Let’s say you typically power up your robot vacuum to run when you go to the gym each morning — if you forget, Alexa may offer to run it for you. It doesn’t always need to ask your permission first , and the feature is enabled by default on Echo devices. You also have the flexibility to manually set which Hunches you want and disable those you don’t need. It’s a fantastic feature for people like me who can’t stick to a fixed daily routine.

The second perk is that you can change Alexa’s wake word . Part of the reason Amazon chose that name (besides the dorky historical nod) is that it claims there aren’t many words with “x” in them, which reduces the likelihood of the voice assistant mistaking random words for its activation phrase. Great logic, but it’s a nuisance if there’s anyone in your home actually named Alexa or Alex. You don’t get complete freedom to rename it (it would be far funnier if you could), but you can choose between Amazon, Computer, Echo, or Ziggy. I have mine set to Computer, naturally, so I can feel like Captain Picard every time I bark commands at it.

Alexa works with just about anything

Amazon Alexa is also by far the most popular and widely used ecosystem compared to Google Home and Apple Home, largely propelled by its wide-reaching device compatibility. There are a dizzying amount of Alexa-compatible products on the market — over 100,000 as of 2020, according to a Statista report . This includes Amazon-owned devices such as Fire TVs and Ring doorbells and security cameras and third-party products like Sonos audio systems. 

Some Echo devices — specifically the fourth-generation Echo and the Echo Show 10 — also include a built-in Zigbee radio. This is essentially a wireless protocol for smart devices similar to Wi-Fi or Bluetooth that allows them to connect directly to Zigbee devices like Philips Hue lighting systems without purchasing a dedicated hub. And device compatibility should only improve as Amazon continues to roll out support for Matter , the new smart home standard designed to make smart devices work with each other across platforms and ecosystems. The fourth-gen Echo can already be used as a Thread border router .

The wide variety and sheer volume of Echo devices and Alexa-compatible gadgets make Amazon the ideal ecosystem for most folks looking to set up their first smart home. If you have specific products or tasks in mind, such as robot vacuums or a gadget that can open / close your curtains , you’re almost guaranteed to find something compatible with Amazon Alexa. And if you can’t, you can always buy Alexa-compatible smart plugs and just use those to control tech or appliances that you already own. I have around a dozen Ohmaxx energy monitoring smart plugs around my apartment that I use to connect regular non-smart devices with my Alexa routines and tell me how much electricity I’m using throughout the day. I have to check this via the dedicated Ohmaxx app, though Alexa does also offer its own Energy Dashboard to monitor energy consumption across a selection of supported products — a service that neither Apple nor Google provides.

In fact, those plugs were the only additional devices in my teeny Alexa setup until fairly recently. I still only own a handful, having recently added some cute Philips Hue and Twinkly smart lighting around the place (which I have set to turn on at sundown every evening). But I now have a fairly lengthy list of additional products I’m waiting to purchase and build into my smart home, such as a Tado smart thermostat and an August Wi-Fi Smart Lock .

Opting for Amazon Alexa is also a no-brainer if you frequently shop via the Amazon website, especially so if you’re a Prime member. I can use voice commands to add and purchase items in my Amazon basket using the default payment and delivery address in my Amazon account and monitor the status of my deliveries either through audio notifications or on-screen tracking on my Echo Show. You can also ask it to keep track of items in your wish list and have it notify you when they go on sale. For better or worse, this is clearly a smart home assistant created by an e-commerce company.

“Alexa, stop trying to sell me things”

I say that because one of the most common complaints about the Amazon Alexa platform is how often it advertises its other features or tries to sell you something. For example, Echo Show devices frequently display homescreen ads , which are almost impossible to remove. Subscribing to Prime doesn’t prevent these from appearing, though it does prevent Alexa from constantly trying to tempt you with Prime-only services.

When you’ve asked Alexa something, it also loves to chime in with “by the way…” which is your only warning before it starts pitching recommendations for Amazon skills, features, and products like some soulless sales rep sniffing out a commission. I eventually learned you can reduce how often this happens by heading into your account settings, selecting “Notifications,” and disabling everything under the “Things to Try” tab . Before that, I lost count of how often I found myself screeching “COMPUTER, SHUT UP!” over its sorry attempts to get me to download a skill for telling crap dad jokes.

There are other issues that might mean Amazon isn’t the right fit for your blossoming smart home empire. The company’s repeated privacy issues will likely be the biggest deterrent for most, with Alexa having previously come under fire for commercial surveillance . Other Amazon products don’t necessarily paint a better picture for the e-commerce giant, which has been criticized for handing over Ring security footage to police without a warrant and acquiring robot vacuum companies to map out its customers’ homes .

Alexa also requires an active internet connection to work and doesn’t support Google Play or YouTube Music for obvious reasons, so if you’re deeply invested in either of those services, then Google Home may be a better fit. Some users may additionally find the platform’s routines to be too restrictive if, say, they need to add specific conditions to automations. Systems like Home Assistant paired with a Raspberry Pi work locally and provide far greater control over automations than Amazon, though setting that up might be intimidating for folks with minimal smart tech experience. Thankfully, there are several ways to integrate the two systems and use Alexa as your voice assistant for controlling Home Assistant devices.

But I don’t really need the granular control the more complex Home Assistant provides. I like the quick and simple convenience of Alexa. It’s easy to set up and supports every kind of device and feature I personally need to automate tasks around my home. Sure, I’d love it if it handled more complex routines and automations natively, but most folks have no need (or desire) to micromanage the exact conditions in which a motion-activated porch light should turn on or whatever. It’s the ideal smart home system for everyday consumers looking for an “idiot-proof” ecosystem, especially if you’re already subscribed to Amazon Prime.

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Artificial intelligent system for multimedia services in smart home environments

• Open access
• Published: 06 July 2021
• Volume 25 , pages 2085–2105, ( 2022 )

Cite this article

You have full access to this open access article

• Albert Rego 1 ,
• Pedro Luis González Ramírez 1 ,
• Jose M. Jimenez 1 &
• Jaime Lloret   ORCID: orcid.org/0000-0002-0862-0533 1  

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This article has been updated

Internet of Things (IoT) has introduced new applications and environments. Smart Home provides new ways of communication and service consumption. In addition, Artificial Intelligence (AI) and deep learning have improved different services and tasks by automatizing them. In this field, reinforcement learning (RL) provides an unsupervised way to learn from the environment. In this paper, a new intelligent system based on RL and deep learning is proposed for Smart Home environments to guarantee good levels of QoE, focused on multimedia services. This system is aimed to reduce the impact on user experience when the classifying system achieves a low accuracy. The experiments performed show that the deep learning model proposed achieves better accuracy than the KNN algorithm and that the RL system increases the QoE of the user up to 3.8 on a scale of 10.

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Avoid common mistakes on your manuscript.

1 Introduction

Integrated networks are communications networks that use devices from multiple manufacturers, protocols, and service operators. Currently, using integrated networks, it is possible to interact with sensors and everyday devices, deployed in what is known as the Internet of Things. IoT is a concept that is being applied in multiple areas, among which we can highlight Smart Home, Smart Cities, Smart Agriculture, Smart Grid, Smart Health, and Wearables.

One of the areas in which the application of the IoT is increasing day by day is in Smart Home. According to [ 1 ], in 2015 the smart home sector had a global value of $ 47 billion and they forecast a growth of 14% per year in the period 2016 to 2022. According to data from the observatory [ 2 ], the Italian market for the smart home sector in 2018 had purchase estimates of 380 million euros, which represents an increase of 52% compared to 2017. In addition, it indicated data from other European countries such as Germany (1.8 billion euros, an increase of 39%), the United Kingdom (€ 1.7 billion, 39% increase), France (€ 800 million, 47% increase) and Spain (€ 300 million, 59% increase).

We can define Smart Home as a system that allows the monitoring and control of a home or office easily. Its application is carried out by both companies and individuals. In order to use it, we must have access, through an integrated network, to the devices located in the home or office. Its implementation can be very broad and located on very different devices. It can go from simple monitoring with cameras for security control, to something as complex as the use of smart refrigerators, which can place orders automatically, when they detect the lack of food. Its most common applications are the control of lighting, gas, water, air conditioning, doors and windows, security cameras, weather sensors, irrigation the home theater, etc. Some of these applications are multimedia applications. In multimedia services, the Quality of Experience (QoE) is the most important metric, since it provides a measure of how good a service is from the user’s point of view [ 3 ].

If we observe carefully, we will realize that the use of electrical energy is necessary for most of the activities that we carry out in our homes throughout the day. We must be responsible with the use of energy due both to the depletion of certain energy sources, and to the impact that occurs on the environment. On a regular basis, at home, we have a large number of appliances such as a refrigerator, freezer, dishwasher, washing machine, dryer, oven, stove, etc. that we use even several times a day. These appliances can often account for half of the energy consumption in the home. Also, it is important to control the consumption of light and the appliances used for air conditioning. In addition, the consumption of electronic devices, in general, is usually higher than it should be. In general, our televisions, multimedia players, etc have a system called stand-by that allows them to never be unplugged from the current, so their electricity consumption is permanent.

Artificial Intelligence can be used to improve and optimize energy consumption in our homes. One of the biggest keys to using AI is its learning phase. The key to optimizing our energy consumption at home is data. As a smart home is an automated environment, we can monitor and get to capture the patterns of daily activities, which are provided by sensors through information technologies. The greater the amount of information available about our habits, the greater the adjustment in energy consumption savings can become. Using learning, automatic or supervised, we can make the control devices capable of self-programming. In addition, we can apply Deep Learning, which allows us to use logical structures very similar to those of organization of the nervous system of a mammal. By using Deep Learning on the control devices that we have at home, we will be able to optimize energy consumption in our smart home.

In this paper, we present an intelligent system in Smart Home environments that manages the set of nodes and services of the environment, enabling or disabling them based on the predictions of user’s service consumption. This system is aimed to improve the results of a deep learning classification system when the algorithm is still learning from the user. Deep Learning works better with a huge number of data and our proposal, based on RL is aimed to provide better flexibility to the system.

The rest of this document is structured as follows. Section 2 presents some of the most relevant works related to our study. The network and system environment and architecture are described in Sect. 3 . Section 4 briefly defines the data preprocessing and the classification model, based on deep learning. Section 5 describes the RL algorithm and all its components. Section 6 presents the evaluations of the system. Finally, Sect. 7 concludes the paper and presents some future works.

2 Related work

There is a large number of articles in which the authors have studied smart homes.

Some authors like [ 4 , 5 , 6 , 7 , 8 ] present studies on IoT and Smart Home. Vasicek et al. [ 4 ] present the IoT concept in a Smart Home. They use IoT devices to create a smart home, without the need to rebuild the home. Jie et al. [ 5 ], present a highly scalable architecture, applying IoT technologies, where they can integrate many applications using a uniform interface, to develop a smart home. Khan et al. [ 6 ] present the design of an IoT Smart Home System (IoTSHS), which was designed, programmed, manufactured and tested with excellent results. The authors stress that it can benefit all parts of society by providing advanced remote control for the smart home. Malche and Maheshwary [ 7 ] analyze the functions of a smart home and its applications and introduce an architecture that they call FLIP (Frugal Labs IoT Platform), to build smart homes enabled for IoT. Yang et al. [ 8 ] present a study examining service characteristics of smart homes in 216 samples in Korea. They also do a study of personal characteristics in the behavior of users.

There are authors like [ 9 , 10 , 11 , 12 , 13 ] who present reviews about IoT and Smart homes. Risteska Stojkoska and Trivodaliev [ 9 ] propose a holistic framework, which incorporates different components from IoT architectures/frameworks proposed in the literature. In addition, they identify a management model for the proposed framework, identifying the tasks to be carried out. Alaa et al. [ 10 ] present a review study, where they formalize a taxonomy focused on three areas, (1) smart homes, (2) apps, and (3) IoT in three major databases, namely, Web of Science, ScienceDirect, and IEEE Explore. Kuzlu et al. [ 11 ] present a review where they compare wireless and wired communication technologies in local area networks applied to Smart homes, taking into account the standards, protocols, data rate, coverage, and adaptation rate. Kamel and Memari [ 12 ] present a review, whose main objective is to classify the different types of Smart homes into three main groups, from the energy point of view. Groups are established at homes with energy monitoring systems, systems with control capabilities, and systems with advanced data processing capabilities. Finally, Jia Chen et al. review, in [ 13 ], the use of IoT in*home systems and applications for health monitoring. They reviewed the key factors that caused the growth of IoT monitoring at home. Then, they presented the lastest advances of the architecture of these systems. Lastly, they discussed future outlooks and personal recommendations.

Other authors [ 14 , 15 , 16 , 17 , 18 ] present studies from the point of view of access security to IoT devices in smart homes. Apthorpe et al. [ 14 ] present the study of four Smart home IoT devices. They find that network traffic rates can show user interactions, even when the traffic is encrypted. For this reason, they indicate the need for technological solutions to protect the privacy of users. Augusto-Gonzalez et al. [ 15 ] present the GHOST framework (Safe-Guarding Home IoT Environments with Personalized Real-time Risk Control), which aims to provide cyber security to residents of smart homes. They do it through a new reference architecture, for smart home security and its users. Lin and Bergmann [ 16 ] establish key requirements to give reliability in smart homes in the future. They propose a gateway architecture, to have a high availability of the system and devices with limited resources. Meng et al. [ 17 ] present the most popular architecture for smart home platforms, detailing the functions of each of its components. They also comment on the main security and privacy challenges of the platforms and review the state of the art of the proposed countermeasures. Ammi et al. [ 18 ] propose a novel Blockchain-based solution for secure smart home systems, using a combined hyperledger fabric and hyperledger composer. Another important aspect of the proposed solution is the mapping of the attributes of a smart home to those from the hyperledger composer. This mapping allows for a customized, designed-for-purpose solution that can meet the security requirements for IoT-based smart homes.

Atat et al. [ 19 ] present the cyber-physical systems (CPS) taxonomy by providing a broad overview of data collection, storage, access, processing, and analysis. These systems are foreseen to revolutionize our world via creating new services and applications in a variety of sectors, such as environmental monitoring, mobile-health systems, intelligent transportation systems, and so on. This is the first panoramic survey on big data for CPS, where their objective is to provide a panoramic summary of different CPS aspects. Also, they provide an overview of the different security solutions proposed for CPS big data storage, access, and analytics. In addition, they discuss big data meeting green challenges in the contexts of CPS.

There are authors [ 20 , 21 , 22 , 23 ] who study energy management in the Smart home environment. Collotta and Pau [ 20 ] propose an energy management system for smart homes, using Bluetooth Low Energy (BLE) [ 21 ] for communications, together with a home energy management (HEM) scheme. Their results show the efficiency of the proposed system, since they reduce the peak load demand, and the charges for electricity consumption, thus increasing the comfort of its users. Al-Ali et al. [ 22 ] present an Energy Management System (EMS) for smart homes. They use Business Intelligence (BI) and Big Data analytics software to manage energy consumption, satisfying consumer demand. Xia et al. [ 23 ] propose an edge-based energy management framework, which provides a low cost of electricity and saves on infrastructure construction. They have implemented a prototype, the results of which show a reduction in the cost of electricity of 82.3%, compared to similar cases. Celik et al [ 24 ] present a review of the antecedents in modeling of residential load, demand-side management (DSM) and demand response (DR), in the settings of a home and in a neighborhood area. The objective is to classify the structure and coordination techniques of energy management, from previous research. Authors as Wu et al. [ 25 ] discover the relations between the trend of the big data era, and that of the new generation green revolution, through a comprehensive and panoramic literature survey in big data technologies toward various green objectives and a discussion on relevant challenges and future directions.

Amjad et al. [ 26 ] propose a cognitive edge-computing-based framework solution, to integrate the advancement of edge computing resource requirement schemes as well as the resource allocation schemes found in the literature for enterprise cloud; to attain a universal resource allocation framework for IoT. Others Authors as Jararweh et al. [ 27 ] present a novel experimental framework for IoT-based environmental monitoring applications, using concepts from Data Fusion (DF) and software defined systems (SDS). It is built on top of the software defined networking platform where the core components (the host, switch and the controller) are expanded to support other software defined systems components (such as software defined storage and security) and enable the applications of different DF techniques in IoT environments.

Studies [ 28 , 29 , 30 , 31 , 32 ] related to the application of AI in the field of Smart Home can also be cited. Sodhro et al. [ 28 ] state that the convergence of IoT and AI promotes energy-efficient communication in smart homes. Its main objective is to optimize the Quality of Service (QoS) of video transmission, which is carried out using wireless micro medical devices (WMMD), in smart healthcare homes. Guo et al. [ 29 ] make reviews of the literature and existing products to define the functions and roles of AI in Smart homes. They point out the existence of a delay between the literature and the products. Sepasgozar et al. [ 30 ] reviewed the applications of the IoT in homes, to make them intelligent, automated and digitized in many of its aspects. They have studied the literature on the use of IoT, AI and geographic information systems (GIS) in smart homes. They state that there is a considerable gap in the integration of AI and IoT and the use of geospatial data, in the field of Smart Home. Song et al. [ 31 ] present frameworks of centralized and distributed AI-enabled IoT networks. Key technical challenges are analyzed for different network architectures. Deep reinforcement learning (DRL)-based strategies are introduced and neural networks-based approaches are utilized to efficiently realize the DRL strategies for system procedures. Different types of neural networks that could be used in IoT networks to conduct DRL are also discussed. Lloret et al. [ 32 ] proposed an intelligent system for detecting elderly problems and assist them. They proposed a communication architecture and designed a software application.

Several works have been carried out about Smart Home from different points of view. However, the role that an automated intelligent management system can play in Smart Home environments needs to be discussed. Consequently, we propose a new role for AI in this scenario. Our proposed system works along with the user to reduce the intrusion that an automatized service management can introduce to the user’s experience. Moreover, the system is oriented to especially reduce the impact of bad predictions on multimedia services, trying to guarantee a good QoE from the user’s point of view. In order to overcome the possible difficulties that a deep learning classifying method may provide to the system, we introduce the use of an RL-adapted method into the Smart Home. RL has been used in other works like in [ 33 ] due to its performance.

3 Proposed architecture

In this section, the architecture of the proposal is detailed. First, the network architecture is explained. Then, the intelligent system is detailed, explaining the function of each module that composes it.

3.1 Network architecture

The architecture of this proposal is an architecture with centralized management based on AI [ 34 , 35 ]. This architecture is divided into five logical layers to organize and maintain divided the functions of each object connected to the network. The management layer (Layer 4) centralizes the information and stores the data of the features of the connected objects (dataset) and the parameters and statistics of the network. The AI uses this information to create workgroups (grouping) and routing, and other functions that keep the network operational. The multiprotocol IoT Gateway is the device in this layer in charge of doing this work and has the capacity to handle different interconnection technologies, store information, host an AI, process information and control internet access. In the internet layer (layer 5), the AI within the cloud can choose the IoT Platform according to the type of parameter and the capacity to process large volumes of data. The rest of the layers are located below as Artificial Intelligence Assistants (AIA) in layer 3, smart things (th) in layer 2 and smart sensors and actuators in layer 1. In this way, the functionalities are separated and kept as a stand-alone system. Fig. 1 shows the hierarchical distribution of the architecture with the objects represented as nodes.

IoT Smart architecture

This architecture is used to design Smart IoT-Networks, which contain interconnected objects with integrated AI (Smart Things). Some of these networks are Smart Home, Smart Office, Smart City, Smart Factory, among others. For this case study, the scenario is based on the connected objects in a Smart Home. The IoT Gateway's AI classifies the objects connected in the network into workgroups and roles by layer [ 34 ], and then, when an object requires resources, the IoT Gateway routes it, selecting the best node to provide them. The AI creates these groups to provide an automatic service to a user based on their features of functions, resources, and capacities. The following is a case of service within the Smart Home, e, g., when a visitor arrives at the house, the AI selects the objects with nearby features to provide this service. The system searches inside the house if there are users or not and automatically attends the door's visit. The necessary resources for this service are multimedia such as video and sound necessary to show the image of the visit on the objects that have this resource and that are close to the user inside the house. If there are no users inside the house, it will send them to objects or mobile devices in any location. One function that would be activated would be facial recognition and identity verification and then sent to be processed in the cloud. The first object to interact in the service would be the smart main door at the front of the house. This would be the requesting node for the resource and would execute the recognition and verification function. The IoT Gateway's AI would be in charge of distributing and transmitting the video and voice to the objective objects that have this resource and that meet the condition of being close to a user and with the capacity to reproduce it. Fig. 2 shows a case study for a workgroup that attends the door's visit service in a Smart Home through this architecture. This workgroup is organized according to the layers of the architecture in Fig. 1 . When the smart door attends a visit, it activates a request to send video and image processing data. The image processing data (facial recognition) is sent through the AI interfaces until the visitor's identification is obtained. The video is sent to each connected object with multimedia playback functions that is close to the user and routed through the IoT Gateway. Therefore, the smart home announces a visit and the identification of a person located in the main door. If the user is away from home, the IoT Gateway will send the information through the cloud to the closest objects with multimedia functions to the user (E.g., Smart Car, Smartphone, Tablet).

Case study with a single workgroup

In Smart Home, with this architecture, the IoT Gateway, through the IoT nodes, can provide multimedia services. Through user requests given to the smart assistant placed in the IoT Gateway, either by voice commands or either by a smartphone application, the user can play their favorite music in the audio system on the distributed speakers in the house, watch movies-on-demand, or automatic record surveillance videos of the house.

3.2 Intelligent system architecture

Once the network architecture has been explained, the system architecture must be discussed. The aim of the system is to improve the QoE of the services in the Smart Home, focusing on multimedia services. In order to achieve that, the intelligent system must enable or ask the user to enable the services the user may want to use after other services and learn how to disrupt the user’s activity the least number of times.

The intelligent system is composed of several modules, located in the IoT Gateway, that are interconnected to provide the desired functionality. The architecture of the system is depicted in Fig. 3 .

Intelligent system architecture

The first module in the system is the recording module. This module is not an intelligent system, but it is the necessary first step to get the data the system needs. Each time the user consumes a service, the IoT Gateway records a data array with the structure shown in Table 1 . Due to space constraints in the table, the user id column has been omitted and fields like the time, the options or the duration are shown as unique columns. However, in the dataset, they are split. For instance, the time and duration fields are split into day (only regarding the time column), hour, minute and second. As regards options, the entry is a set of columns, from option 1 to option 5, represented by binary values. The meaning of the options is meaningless for the system. Only the service finds this meaning useful. In Table 1 , the first row means that the user enabled the heating service the second day of the week at 5:39 PM for 1 hour 42 min. Moreover, that day was a working day (the type of day field shows us this) and that service is not a multimedia service. The options here show us that the system was enabled in heat mode. The IoT Gateway manages equivalency tables to transform the meaning of these fields into their values. Consequently, the smart system works with integers and that makes easier the processing of the data. Table 2 shows an example of an equivalency table. The options used in this record can be non-exclusive options.

The second module of the system’s architecture is the data preprocessing module. This is a software module that computes the data to transform it into the input of the next modules. The data preprocessing process is detailed in Sect. 4 .

After the data has been processed, the datasets extracted from the logs provided by the record module are sent to the classifying module. Here we have to distinguish between the training phase and the prediction phase. In the training phase, the dataset extracted is used to train the classifying. Therefore, a training, validator and test dataset are extracted. Once the classifying system has been trained, the classifying module is used to predict the next service to be consumed. Consequently, the data sent by the data preprocessing module are the next inputs for the classification. In that case, the classifying module returns the predicted service.

The next module in the system is the RL module. The RL module receives information about the services from the data preprocessing module. This information is used to build the initial states and to calculate the required metrics. For instance, the RL module needs to know if a specific service is a multimedia service. When the classifying module predicts a service consumption, that prediction is an input for the RL module. The RL module chooses the best action, as explained in Sect. 5 , and that action is the output of the RL module.

Finally, the IoT Gateway has an actuator module that performs the action chosen by the RL algorithm.

In the next section, the data preprocessing and the classifying modules are described.

4 Preprocessing and classifying algorithms

In this section, the data preprocessing and the classifying modules are explained. First, the data preprocessing process is described. Its process and algorithms are detailed. Then, the classifying model chosen is described.

4.1 Data preprocessing process

Once the logs are provided to the preprocessing module, this module starts extracting some characteristics from them. Firstly, the RL module uses some data characteristics that are outputs of the preprocessing module. For the RL module, the statistical probability of changing from one state to another and the mean of the timestamp when it does are important data. Consequently, the preprocessing module transforms the data extracting these statistics. In order to achieve this, the module manages Markov chains. The definition of these chains adapted to the problem is described in (1):

Where \({X}_{n+1}\) is the next service consumed, \({X}_{n}\) , the service consumed in the iteration number \(\rm {n}\) and \({x}_{n}\) is the service number \(\rm {n}\) in the services set \(\rm {S}\) .

In this system, the data will be processed regardless the time. That means, regarding the preprocessing, the time does not change the probability of the transition between services. That is depicted in (2):

The fact that the Markov chain does not consider time to set the probability does not mean that in the system the time is not considered an important input. However, the RL algorithm will use time in a different manner.

Once the records have been read, the preprocessing module turns them into matrices so that the RL algorithm can operate efficiently with them. The first data the RL will need is the probability of consuming a service. To set this probability, depending on the last service consumed, the preprocessing module builds a transition matrix. This matrix, given a consumed state \(i\) and a possible next consumed state \(j\) , defines the probability \(p\) of demanding \(j\) . The preprocessing module must satisfy the constraints defined in (3) and (4):

Where \( p\left( {i,j} \right) \) is the probability of consuming the service \( j \) after consuming the service \( i \) .

In this system, the probability of consuming a service is not enough. Another important statistical data is the time between transitions. If the time is not considered, the system could ask for the right service hours before the user wants to enable it. This, although will not be treated as a feature of the definitions in the RL algorithm, will be necessary information to implement some of the actions of the RL system (see Sect. 5 ). For these calculations, the preprocessing module uses the time and duration of the records for each service. As regards the means of time and duration, and to take advantage of the incremental processing of records, the calculation will use a recursive formula. In order to calculate the mean, the equation defined in (5) is used.

Where \({\rm {\mu }}_{n}\) is the media with \(n\) records, \(n\) is the number of records and \({t}_{n}\) is the time or duration of the record number \(n\) . From this equation, we need to define the basic case as in (6):

The variance is calculated in a similar way. The recursive formula described in (6) is used to avoid iterating through each past record when a new service consumption is ended.

With these definitions, we can set the algorithm of the data preprocessing module. This algorithm is shown in Fig. 4 , in a flow diagram that describes the algorithm. First, the data needed is initialized. Then, all the records are processed until there is no more records left. For each day of the week, that is why the next condition is compared with 7, the Markov and the stats are calculated. The pseudocode of the algorithm is described in Algorithm 1. This algorithm defines the main procedures of the data preprocessing module. Given a set of records from the IoT Gateway, the module processes the records to assign them to users and days (User_Records). Then, the Markov transition matrix (Markov) is calculated. This is done in an iterative way. The algorithm of the Markov data building is shown in Algorithm 2 and explained later. Then, the other statistics needed are extracted from the time and duration data. This subroutine is explained in Algorithm 3. Finally, the datasets for training and validating the classification module are extracted.

Data preprocessing algorithm

As regards the Markov structure, it is calculated as it is shown in Fig 5 , that depicts the flow diagram of this algorithm. In order to calculate the transition matrix, we need to know how many times a certain service is consumed after another one. We need to store the data in the Markov structure. In Fig. 5 , it is shown how this info is read from the record. The structure is indexed based on the day, the first service and the second service, which is consumed after the first one. Moreover, for each first service, we need the total amount of transitions, totalCases. If we find a record and the structure is not created, we need to create it. And then, the totalCases is set to 1, as shown in Fig. 5 . . If that service is the first time that is consumed, the following service consumed has a 100% of transitions. Otherwise, we need to iterate through all the previous transitions from that service to calculate the probability for each second service. That is shown in the last loop in Fig. 5 . Algorithm 2 describes the same process with more detail.

Markov transition matrix calculation algorithm

Finally, we find the statistics calculation in Fig. 6 . Fig. 6 shows the flow diagram that corresponds to Algorithm 3. It depicts the two different ways of calculating the statistics. If there was no previous statistics for a specific day when the record is read, the data structure is created and initialized Otherwise, the statistics must be recalculated. By using equations ( 5 ) (6) and (7) these statistics can be recalculated each time a new service is consumed after another one in the same day or each time the new service ends its consumption (for calculating the duration).

Statistics calculation algorithm

In the following subsection, the classifying module is explained. That module uses some of the data provided by this module. In the training phase, the records are split to create the datasets. In the prediction phase, each new record is sent to the classifying module.

4.2 Classifying module

The classifying module is based on neural networks. Then, we have a deep learning method to predict the next service consumed by the user. The entries of the system will be the different measurements of the record (time, day, type of day, service consumed, duration and so on). In the records, we have 20 different features. As output, the different services provided. We define seven different services, shown in Table 3 . A weak point of having a classifying system as the single method to predict service consumptions is that if a new service is added to the system, the classifying model has to be redefined and trained again. However, with an RL as a supervisor, that process can be delayed, and a provisional action can be added to avoid a QoE reduction until the classifying model is trained again.

The classifying model will be based on a neural network whose architecture is depicted in Fig. 7 . We define a number of neurons in the input layer equals to the number of features in the data. In our case, we will have 20 features, so 20 different neurons in the input layer. The number of hidden layers, \({n}_{h}\) , and the number of neurons in each hidden layer, \({m}_{h}\) , will be parameters of the model. After testing the model varying these parameters, the model used will be the one with the highest accuracy. This evaluation will be presented in Sect. 6 .

Classifying neural network architecture

As regards the output layer, it will be composed of 7 neurons, one for each class to be detected by the system. If the number of different services in the Smart Home increases, the system would need to extract more relevant features and the model would need to be adapted. However, this adaptation is not going to be considered in this paper, and it will be considered as future work.

The loss function will be cross-entropy and the optimization method will be Root Mean Square Propagation (RMSprop), which achieves good results in multi-layer neural networks [ 36 ].

Another aspect of the model that has to be chosen is the activation function. We will use the ReLU function as the activation function of the hidden layer, \({a}_{h}\) [ 37 ]. The softmax function will be used as the activation function of the output layer, \({a}_{o}\) , to get the probabilities of belonging to each class [ 38 ].

With the model presented, the next service consumed by the user can be predicted. In the next subsection, we define the RL module, that will choose the best action to perform based on this prediction.

5 Reinforcement learning module

This section describes the RL algorithm that will be used to reduce the impact of bad predictions. First, the environment, the states and the actions are defined. Then, the data structures that the algorithm needs to work are described. Finally, the rewards calculation and the policy of the system are detailed.

5.1 Environment, states and actions

In this subsection, the environment, the states and the actions that the algorithm will use to implement the reinforcement learning will be described.

Firstly, we have to define the environment. The reinforcement learning will act in the Smart Home environment. Exactly, it will notify the IoT Gateway which command must perform. Therefore, the agent of the reinforcement learning algorithm will be the IoT Gateway. Initially, it could be thought that the user would be the agent, but the user will be only the source of information of the actions performed by the agent. For instance, when the user consumes a service and the algorithm decides to start another service after a certain amount of time, the user may not need that service and give the order to turn it off. The user, then, is an input for the algorithm. In this case, the user is saying that the action performed was not chosen correctly. This fact would affect the reward of the action. We will discuss that in the next subsections. For now, it is important to notice that the user will be an input, not the agent that modifies the environment. Despite this fact, the user is the one who starts enabling the services. Consequently, if the definition of the states would be only based on the current service running in the Smart Home, the definition of the environment and the agent would be more complicated. Furthermore, the algorithm would be incomplete, due to the fact that we need to know which service is supposed to be necessary to activate. It is there when we need the classifying module. Moreover, due to the user patterns, the day and the current time are also relevant data.

From the previous discussion, the following definition of the states is obtained. The RL algorithm will decide which action needs to be performed based on the current service consumed, the next service predicted and the current day. This presents a problem that will not be addressed in this paper: how new states are generated through the use of the system. We will consider in this paper that the states are statics and are generated from the transition matrix obtained from the preprocessing module. However, new patterns can be adopted by the user and this will be discussed in future works.

Usually, the states of an RL algorithm can be depicted in a state diagram. However, in this scenario, the state diagram can be composed of a high number of states, depending on the number of services. This can make the algorithm not scalable. Nevertheless, the Smart Home environments do not have a high number of services. Furthermore, not all the services can be important enough to define a state. In this paper, we will use all of them but, in future works, a categorization of services can be proposed to reduce the space of states.

Fig. 8 shows an example of a diagram of states with three different services to enable. In this case, there is no probability of transitioning from state 2 (S2) to state 3 (S3). Therefore, there is no transition between those states in the graph. Furthermore, depending on whether the user actually consumes the predicted service, the next state might be with the same service being consumed. That may happen when the classifying module does not predict the next service accurately or when the prediction was to consume again the same service. The state diagram is not, then, a direct representation of the Markov chain derived from the transition matrix. There are transitions to states with the same current service that do not represent the same service being consumed twice. In Fig 8 , in order to make easier the readability of the diagram, the states are composed only of the current service. However, the diagram is more complex because the algorithm defines the state as a pair of states. The first state is the current state and the second one is the predicted state. Fig. 9 depicts the diagram of states of the state S2 of Fig. 8 . Within each state of Fig. 8 , there would be a subdiagram with similar transitions. Although this can add some difficulty to understand the algorithm, the number of states is finite and not big enough to present a deep-learning algorithm for the policy function as in [ 39 ]. However, it could be a future work to study.

State diagram with three services

Subdiagram of state S2

For each state defined, a set of actions can be performed. The actions, however, must be classified based on how much they interrupt the user’s activity. This helps the algorithm to not reduce the user’s QoE as much if the predictor fails to predict the next service. For this proposal, six different actions are going to be defined and classified depending on this intrusion level. The actions are shown in Table 4 , where the level of intrusion and the description are detailed. The simplest action is to wait, without executing any service. This has the lowest impact on the user because they do not have to do anything. However, this does not mean it is always the best action, because sometimes enabling a service or turning on a node can reduce the waiting time for the user or implying other advantages (for instance, saving energy or heating up the house before the user arrives). An alternative with low impact would be to ask if the user wants to enable the predicted service or another one from a list of similar alternatives (services from the same group). This is not too different from a manual service selection. If the system only asks to enable a certain service or it turns on a node it will have a higher intrusion from the user’s point of view. We have to take into account that certain services are more intrusive than others. This is reflected in the actions. The system can automatically start a predicted service, with some options or subfunctionalities that are not too intrusive, such as perform a search on Internet or increase or decrease the temperature a few degrees. However, other services, like opening a door, turning on the TV or start playing a song or video have a bigger impact on the user. Finally, stopping services that the user is using or turning off nodes automatically has the biggest impact on the user, so transitions that require disable services will have the biggest level.

The definitions of the actions are not simple actions as they could be found in other RL algorithms, but there are actions that will depend on the predicted service. That means, that enabling a node will enable a node that provides the predicted service, starting a service will start the service predicted or a module of the service and so on.

These actions provoke changes in the network performance, and, depending on the next values obtained from the Statistic Analyzer, the reward value of the action taken will be updated.

The way the rewards are assigned and calculated and how the actions are performed are described in the next subsections. However, we first need to know the data structures and concepts.

5.2 Data structure

In this subsection, the data structures that the RL algorithm uses are described.

First of all, the RL algorithm needs a structure where the reward of each action for each state is stored. This data structure will be a matrix, where the rows will be the states and the columns will be the different actions of the algorithm. In this case, the states are a pair of current service and predicted service values. Table 5 shows an example of the data structure for three different services, following the same state diagram that is shown in Fig. 8 .

Secondly, the algorithm will need information about the services. This information is given by the data preprocessing module. For each service, the following data is required: the group where the service belongs, the impact that the service has on the user and if the service is multimedia. The group of the service is data that the IoT Gateway knows because it is that agent who categorizes the services attending to the architecture presented in Sect. 3 . If the service is multimedia it also comes from the IoT Gateway. Like the group of the service (workgroup), this is information that is presented in the dataset. However, the last data that is needed, the impact of the service on the user, must be set by the RL algorithm. A simpler classifying module could be added in the system architecture just before the RL module to determine which services have a bigger impact on the user. However, to make the proposal simpler, the IoT Gateway provides the RL module with this information, obtained statically from the group of the service. Table 6 shows an example of this data structure, which we will name as infoServices. The impact field is a positive integer. The bigger the impact value, the more intrusive the service is.

The last data structure needed to implement this RL algorithm is the Input User Matrix. This data structure will store how much a user input is needed for each action. That means, that the algorithm will be able to know the impact of having a certain input for the user for each action chosen. This will be useful for knowing how much a certain action was appropriate for a state, that is, how the RL algorithm should be rewarded. For instance, if a set of services is given to the user to choose which one should be started, if the user selects the predicted, the reward will be different from the one obtained if the user discards all the possibilities. This will be discussed in more detail in the next subsection, where the policy of the RL algorithm is detailed. Table 7 shows an example of this matrix.

5.3 Rewards, policy and objective function

In order to define the reward properly, we have to analyze the problem we are dealing with, because it is the problem, and the scenario, the one that defines the appropriate method to obtain and calculate the reward. The reward will indicate to the system whether the action chosen was effective in the same state or, otherwise, if there are better options. If reinforcement learning is applied to a game, the effects that a performed action over the player determines the reward. If the game provides an actual reward such as finishing a level or defeating an enemy, the reward will be positive. If other actions usually drive to losing a life, losing objects and so on, the reward will be lower and will decrease if we choose that action, even being able to contain a negative value.

In the environment previously defined, the goal of the system is to avoid the user from enabling services or nodes. However, it is also important to reduce the intrusion of the system, especially regarding multimedia services. Therefore, the input of the user will be quite important to know if the action chosen by the system was appropriate.

Applying the RL system to the result of a classifying algorithm modifies the way the reward is calculated. In this case, the reward will be a measure of the number of times the classifying module has predicted correctly which service would be consumed. Therefore, if the module provides high accuracy with certain services, the actions with a high level of intrusion can work well. On the other hand, when it has low accuracy, performing an action with a low level of intrusion could be a better option.

The algorithm has a set of actions, \(J\) . We are going to describe the general case where several actions can belong to the same level classification. However, this definition will also be valid when there can be only one action per level. Consequently, we can define the reward obtained after choosing an action \(j\) as in (8).

where Input(User) is a parameter that returns the IoT Gateway and its value depends on the action chosen and the action the user does after. The possible values were defined previously, as an example, in Table 7 . Moreover, \(\beta \) is a threshold defined to classify the action performed by the user as corrective or not. Then, if the action was corrective, the reward should be decreased.

Nonetheless, if we only consider the reward like that, we can drive the system to a point where the actions of less level, due to the fact that they are less intrusive, present a higher reward. In order to solve this, the reward should be incremented regarding the level of the action chosen. Consequently, the reward given to an action j in the iteration \(i\) will be defined by (11).

Where level(j) is the level of the action \(j\) chosen.

The last adjustment that needs to be done is to give extra importance to multimedia services. That means, for those actions coming from a state whose current service is multimedia, the reward should increase or decrease at a higher rate. In (12) we can see this adjustment.

Where \( {\text{ }}multimedia(s){\text{ }} \) is the flag in the dataset that identifies the service \(s\) as a multimedia service.

We can define, then, the total reward of an action \(j\) as (13).

Initially, the rewards are calculated depending on the level and the probability of transitioning from the initial state \({\rm {S}}_{a}\) to \({\rm {S}}_{b}\) as is defined in (14):

We need to define then the policy of the system. In subsection 5.2 we defined a matrix that contains the rewards for each pair of state and action. Usually, RL algorithms choose the action with the highest reward. However, in this case, the policy will have a component of exploration. This component will force the algorithm to try actions that had not been tried for a high number of iterations. Equation ( 15 ) defines the function ρ that represents the probability of choosing and action \(\boldsymbol{\alpha }\) that has less reward than the action with the maximum reward, n, for the same state.

Where i is the number of iterations that have passed since the last time action α was chosen and ω is a parameter that sets a weight to the exploration.

The policy function can be described then by (16):

6 Methodology and results

In this section, the tests performed and the results obtained are discussed. First, the classifying model will be tested in order to obtain goods parameters. Then, the QoE will be measured to check if the introduction of an RL algorithm improves it.

6.1 Classification analysis

In Sect. 4 , the classifying model was defined based on two parameters. These parameters, the number of hidden layers \({n}_{h}\) , and the number of neurons in each hidden layer \({m}_{h}\) , are analyzed in this subsection. In order to get the better values for these parameters, the model will be trained, given the same dataset, for the values of \({n}_{h}\) , and \({m}_{h}\) shown in Table 8 .

The results of the evaluation of the model for each pair of parameters values are shown in Fig. 10 . In the figure, the accuracy for each number of neurons is shown for each model with a different number of hidden layers. The size of the training dataset corresponds to two weeks of user activity. Although the mean of the values of each series could be interesting for other experiments, we only need here the maximum value of all the series to select the values of the parameters with the best accuracy. In this case, the model works better with this kind of data when it has five hidden layers and ten neurons per hidden layer. With those values, the accuracy of the classifying model is 54%. Despite the fact the accuracy might be improved with further feature selection or with larger datasets, this is a good accuracy to prove the hypothesis of this work.

Classification model results

Furthermore, the results of the deep learning model are tested against another classifying algorithm. In this case, we chose the KNN algorithm to implement a classifying algorithm that predicts the next service. In this case, the KNN algorithm is parametrized depending on the number of neighbors. Fig. 11 shows the results obtained from the KNN algorithm for a number of neighbors from 1 to 18. The maximum obtained accuracy, with the same dataset that the deep learning model, is 41.21% with three neighbors.

KNN classification results

Fig. 12 shows an example of how preparing the data from the Smart Home environment may help to get good levels of accuracy. The figure depicts the violin graph of the feature “type of day”. This feature has two values: 1 if the day is a working day 0 it is not. Since the services with higher ID are the services regarding multimedia and leisure (see Table 3 ), the probability of having a high value in service is higher when the type of day is 0.

Violin graph of service depending on the type of day

Finally, Table 9 shows the running time of the different parts of the algorithm. We have measured the mean of the running times from the different parts of the proposal. The experiments were carried on an AMD Ryzen 5 5600X with 32GM RAM DDR4. The results show that the times fit the environment restrictions, which are not extremely demanding in terms of running time.

6.2 QoE in smart home results

In this section, the most important metric for this work, QoE at Smart Home, will be calculated for a KNN classifying system, the deep learning system and the deep learning system with the RL as a supervisor.

However, we need to define first how we calculate QoE applied for the Smart Home scenario. At Smart Home, unlike in multimedia scenarios, the delay, bandwidth and jitter will not be our concern. We are going to put aside the network resources and focus on the user. Thereby, the QoE decreases when the user is interrupted with consults about services they do not want to use. Moreover, if the system starts a service the user does not want to consume or to stop a service the user wants to keep consuming, the QoE will also be decreased. After each prediction, the QoE will be updated based on the loss using the function λ (17).

Where \( L\left( {y,\hat{y}} \right) = \left\{ {\begin{array}{*{20}c} {0~\quad if\;~y = ~\hat{y}~} \\ {1~\quad if\;y~ \ne {\text{ }}~\hat{y}} \\ \end{array} }, \right. \) and multimedia refers to the current service.

Note that the loss defined is only applied to the classifying. For the RL algorithm, the loss is defined by the user input matrix, depending on the action chosen.

Therefore, let \(\alpha \) be a parameter to weight the decreases, the total QoE after the \(n\) iterations (transitions) of the system is determined by (18).

In order to maintain similarity with the QoE defined in multimedia, the max value of QoE in our experiments will be 10. As regards \(\alpha \) , it can be adjusted depending on the length of the measurement. We will show three different experiments where the parameter \(\alpha \) is different, but the number of iterations does not vary. The number of iterations will be 88 for each measurement.

Fig. 13 depicts the QoE obtained during all the iterations with an \(\alpha \) value of 20. With this \(\alpha \) value, the RL algorithm obtains a QoE of 8.7. the deep learning algorithm obtained a QoE of 6.56 and the KNN algorithm of 5.89. Although the RL obtains a high QoE, the other two methods are not too far from each other in terms of QoE. Despite the good QoE obtained with the classifying methods without RL, the accuracy of the predictions has been low. This \(\alpha \) value only shows a real behavior when the user cares less about interruptions.

QoE after 88 iterations with \(\alpha =20\)

The QoE values obtained with an \(\alpha \) value of 15 are shown in Fig. 14 . Again, the RL algorithm obtains better QoE than the other alternatives, with an 8.26. In this experiment, the deep learning algorithm obtains a 5.53 of QoE. The KNN algorithm is again the one that provides the worst performance with a QoE of 4.26. These results might represent with more accuracy the average user, whose QoE decreases with the interruptions. In addition, the RL presents the least decrease with the change of the \(\alpha \) value.

QoE after 88 iterations with \(\alpha =15\)

Finally, Fig. 15 illustrates the results when the parameter \(\alpha \) is reduced to 10. In this case, the bad predictions that have as a consequence to interrupt ongoing services decrease quickly the QoE. The RL supervisor, by choosing actions that prevent interruptions when the classifying algorithm does not give an accurate prediction, presents the best QoE with 7.39. One more time, the deep learning classifying model presents better results than KNN, with 3.59 and 1.53, respectively. The decreased pace of the QoE, in this case, may show an unrealistic situation where the user punishes the system too much. However, it shows how the RL algorithm improves the performance of the system.

QoE after 88 iterations with \(\alpha =10\)

7 Conclusion and future work

IoT has provided new ways of networking, communicating and sensor development. In addition, with its introduction, several new applications have been designed in several fields. Smart Home is a new way of understanding services at home. With the communication that this technology offers, users can access new services at home. In addition, AI can help automatizing tasks at home using these services and architectures.

In this paper, we have introduced an intelligent system to automatize Smart Home services management. The aim of the intelligent system was to avoid user interruptions to guarantee a good QoE, prioritizing the multimedia services. In the system, we have designed the data preprocessing process, the classifying algorithm and the RL system that improves the performance, defining all the related concepts needed to describe the scenario and so that the system can interact and provide functionality.

We have also defined QoE for this scenario and we have measured it for different parameters and systems.

Results show that the deep learning classifying model proposed achieves better accuracy than other algorithms like KNN, improving their performance around 33%. The QoE of the deep learning algorithm shown with different values of the parameters has always been higher than the KNN algorithm. With high values of the parameter \(\alpha \) , the QoE obtained is higher, being in the experiment 5.53 for the deep learning algorithm and 4.26 for the KNN. This difference, based on the difference in accuracy, gets higher when the weights of the prediction increase. That is when \(\alpha \) has lower values. This difference raised up until 2.06, with QoE values of 3.59 for deep learning and 1.53 for KNN. Nevertheless, the most remarkable results are the ones obtained from the RL system, which manages the predictions to reduce the impact and obtains QoE values of 8.7 when \(\alpha \) has the highest value and 7.39 when it has the lowest. That makes a difference of 3.17 in the first case and 3.8 in the second one. That shows that the inclusion of an RL system improves the QoE in Smart Home environments when the classifying cannot predict the next service with high accuracy.

There are several aspects that can be studied in future works. Firstly, the system can be evaluated against better classifying models. That would include the study of improving the accuracy obtained in this work. The feature selection could be enhanced, reducing dimensionality [ 40 ] or an automatized label system could be included in the system [ 41 ]. Other parameters, such as the activation function or the optimization parameters could be changed to improve the accuracy. The scalability of the system could be studied. Mechanisms to adapt the system to new services could be defined to efficiently control a bigger number of states. Moreover, the system could be compared to other classifying models, besides KNN. The QoE metric could add an intelligent method to select the \(\alpha \) parameter based on the user profile. Thereby, some actions to correct low QoE could be included in the system. Finally, this system could be adapted to face other problems in IoT, such as video surveillance or Industrial IoT environments, working together with other solutions [ 42 ] or being integrated in other netwokrs like 5G [ 43 ].

Data availability

The data that support the findings of this study are available from the corresponding author, upon reasonable request.

Change history

11 november 2021.

The original online version of this article was revised: Funding note has been added.

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Open Access funding provided thanks to the CRUE-CSIC agreement with Springer Nature. This work has been partially supported by the "Ministerio de Economía y Competitividad" in the "Programa Estatal de Fomento de la Investigación Científica y Técnica de Excelencia, Subprograma Estatal de Generación de Conocimiento" within the project under Grant TIN2017-84802-C2-1-P. This work has also been partially founded by the Universitat Politècnica de València through the post-doctoral PAID-10-20 program.

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Albert Rego, Pedro Luis González Ramírez, Jose M. Jimenez & Jaime Lloret

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Rego, A., Ramírez, P.L.G., Jimenez, J.M. et al. Artificial intelligent system for multimedia services in smart home environments. Cluster Comput 25 , 2085–2105 (2022). https://doi.org/10.1007/s10586-021-03350-z

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IoT Home Automation

In this article, we will discuss the overview of IoT home automation . And will focus on smart lighting, smart appliances, intrusion detection, smoke/gas detector, etc. Let’s discuss it one by one.

• Home automation is constructing automation for a domestic, mentioned as a sensible home or smart house. In the IoT home automation ecosystem, you can control your devices like light, fan, TV, etc. 
• A domestic automation system can monitor and/or manage home attributes adore lighting, climate, enjoyment systems, and appliances. It is very helpful to control your home devices. 
• It’s going to in addition incorporates domestic security such as access management and alarm systems. Once it coupled with the internet, domestic gadgets are a very important constituent of the Internet of Things.
• A domestic automation system usually connects controlled devices to a central hub or gateway. 
• The program for control of the system makes use of both wall-mounted terminals, tablet or desktop computers, a smartphone ​application, or an online interface that may even be approachable off-site through the Internet.
• Smart Home automation refers to the use of technology to control and automate various functions in a home, such as lighting, heating, air conditioning, and security. In the context of IoT (Internet  of Things) and M2M (Machine-to-Machine) communications, home automation systems can be controlled and monitored remotely through a network connection. 
• One of the key benefits of IoT-enabled home automation is the ability to control and monitor a wide range of devices and systems from a single, centralized location, such as a smartphone or tablet. This can include everything from lighting and temperature control to security cameras and alarm systems.
• Another advantage of IoT-enabled home automation is the ability to remotely monitor and control devices, even when away from home. This can be useful for controlling energy consumption and ensuring the safety and security of the home.
• IoT-enabled home automation systems typically involve the use of smart devices, such as thermostats, light bulbs, and security cameras, that can be controlled and monitored through a centralized hub or app. These smart devices can communicate with each other and with the centralized hub using wireless protocols such as Zigbee, Z-Wave, and Bluetooth.
• In addition, IoT-enabled home automation systems can integrate with other smart home technologies, such as voice assistants like Alexa and Google Home, to provide additional functionality and convenience.
• Overall, IoT-enabled home automation can provide many benefits to homeowners, including increased convenience, energy efficiency, and security. However, it is important to ensure the security of these systems, as they may be vulnerable to hacking and other cyber threats.

Components : Here, you will see the smart home components like smart lighting, smart appliances, intrusion detection, smoke/gas detector, etc. So, let’s discuss it.

Component-1 : Smart Lighting –

• Smart lighting for home helps in saving energy by adapting the life to the ambient condition and switching on/off or dimming the light when needed.
• Smart lighting solutions for homes achieve energy saving by sensing the human movements and their environments and controlling the lights accordingly.

Component-2 : Smart Appliances –

• Smart appliances with the management are here and also provide status information to the users remotely.
• Smart washer/dryer can be controlled remotely and notify when the washing and drying are complete.
• Smart refrigerators can keep track of the item store and send updates to the users when an item is low on stock.

Component-3 : Intrusion Detection –

• Home intrusion detection systems use security cameras and sensors to detect intrusion and raise alerts.
• Alert can we inform of an SMS or an email sent to the user.
• Advanced systems can even send detailed alerts such as an image shoot or short video clips.

Component-4 : Smoke/gas detectors –

• Smoke detectors are installed in homes and buildings to detect smoke that is typically an early sign of Fire.
• It uses optical detection, ionization for Air sampling techniques to detect smoke.
• Gas detectors can detect the presence of harmful gases such as CO, LPG, etc.
• It can raise alerts in the human voice describing where the problem is.

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• Articles / Homework

Smart Homework: 13 Ways to Make It Meaningful

by MiddleWeb · Published 08/04/2014 · Updated 11/17/2019

In the first installment of Rick Wormeli’s homework advice, he made the case for take-home assignments that matter for learning and engage student interest . In Part 2, Rick offers some guiding principles that can help teachers create homework challenges that motivate kids and spark deeper learning in and out of school.

These articles are adapted and updated from Rick’s seminal book about teaching in the middle grades, Day One & Beyond: Practical Matters for New Middle Level Teachers . Rick continues to offer great advice about homework, differentiation, assessment and many other topics in workshops and presentations across North America. Check back in Part 1 for some additional homework resources.

I’ve been accumulating guiding principles for creating highly motivating homework assignments for many years — from my own teaching and from the distilled wisdom of others. Here are a baker’s dozen. Choose the ones most appropriate for students’ learning goals and your curriculum.

1. Give students a clear picture of the final product. This doesn’t mean everything is structured for them, or that there aren’t multiple pathways to the same high quality result. There’s room for student personalities to be expressed. Students clearly know what is expected, however. A clear picture sets purpose for doing the assignment. Priming the brain to focus on particular aspects of the learning experience helps the brain process the information for long-term retention. Setting purpose for homework assignments has an impact on learning and the assignment’s completion rate, as research by Marzano and others confirms.

2. Incorporate a cause into the assignment. Middle level students are motivated when they feel they are righting a wrong. They are very sensitive to justice and injustice. As a group, they are also very nurturing of those less fortunate than them. Find a community or personal cause for which students can fight fairly and incorporate your content and skills in that good fight— students will be all over the assignment.

4. Incorporate people whom students admire in their assignments. Students are motivated when asked to share what they know and feel about these folks. We are a society of heroes, and young adolescents are interested in talking about and becoming heroic figures.

5. Allow choices, as appropriate. Allow students to do the even-numbered or odd-numbered problems, or allow them to choose from three prompts, not just one. Let them choose the word that best describes the political or scientific process. Let them identify their own diet and its effects on young adolescent bodies. Let them choose to work with partners or individually. How about allowing them to choose from several multiple-intelligence based tasks? If they are working in ways that are comfortable, they are more likely to do the work. By making the choice, they have upped their ownership of the task.

6. Incorporate cultural products into the assignment. If students have to use magazines, television shows, foods, sports equipment, and other products they already use, they are likely to do the work. The brain loves to do tasks in contexts with which it is familiar.

7. Allow students to collaborate in determining how homework will be assessed. If they help design the criteria for success, such as when they create the rubric for an assignment, they “own” the assignment. It comes off as something done by them, not to them. They also internalize the expectations—another way for them to have clear targets.

With some assignments we can post well-done versions from previous years (or ones we’ve created for this purpose) and ask students to analyze the essential characteristics that make these assignments exemplary. Students who analyze such assignments will compare those works with their own and internalize the criteria for success, referencing the criteria while doing the assignment, not just when it’s finished.

9. Spruce up your prompts. Don’t ask students to repeatedly answer questions or summarize. Try some of these openers instead: Decide between, argue against, Why did ______ argue for, compare, contrast, plan, classify, retell ______ from the point of view of ______, Organize, build, interview, predict, categorize, simplify, deduce, formulate, blend, suppose, invent, imagine, devise, compose, combine, rank, recommend, defend, choose.

10. Have everyone turn in a paper. In her classic, Homework: A New Direction (1992), Neila Connors reminded teachers to have all students turn in a paper, regardless of whether they did the assignment. If a student doesn’t have his homework, he writes on the paper the name of the assignment and why he didn’t do it.

11. Do not give homework passes. I used to do this; then I realized how much it minimized the importance of homework. It’s like saying, “Oh, well, the homework really wasn’t that important to your learning. You’ll learn just as well without it.” Homework should be so productive for students that missing it is like missing the lesson itself.

12. Integrate homework with other subjects. One assignment can count in two classes. Such assignments are usually complex enough to warrant the dual grade and it’s a way to work smarter, not harder, for both students and teachers. Teachers can split the pile of papers to grade, then share the grades with each other, and students don’t have homework piling up in multiple classes.

There are times when every teacher on the team assigns a half-hour assignment, and so do the elective or encore class teachers. This could mean three to four hours of homework for the student, which is inappropriate for young adolescents.

13. Occasionally, let students identify what homework would be most effective. Sometimes the really creative assignments are the ones that students design themselves. After teaching a lesson, ask your students what it would take to practice the material so well it became clearly understood. Many of the choices will be rigorous and very appropriate.

This is one reason I always recommend that, as a basic premise, we avoid Monday morning quizzes and weekend or holiday homework assignments. Sure, there will be exceptions when long-term projects come due. But if we are really about teaching so that students learn and not about appearing rigorous and assigning tasks to show that we have taught, then we’ll carefully consider all the effects of our homework expectations. Our students will be more productive at school for having healthier lives at home.

▶ More resources from Rick Wormeli:

Although Rick never mentions the word homework in this article about helping adolescent students improve their “executive function,” you will immediately see the connections! At the AMLE website .

NEXT: In our final excerpt from Day One & Beyond, Rick Wormeli shares his approach to homework assessment – with an clear emphasis on maintaining teacher sanity.

His books include Meet Me in the Middle ; Day One and Beyond ; Fair Isn’t Always Equal: Assessment and Grading in the Differentiated Classroom ; Differentiation: From Planning to Practice; Metaphors & Analogies: Power Tools for Teaching Any Subject, and Summarization in Any Subject , plus The Collected Writings (So Far) of Rick Wormeli: Crazy Good Stuff I Learned about Teaching Along the Way .

He is currently working on his first young adult fiction novel and a new book on homework practices in the 21 st century.

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Tags: Day One & Beyond grading homework homework homework guidelines homework policies Rick Wormeli why homework

MiddleWeb is all about the middle grades, with great 4-8 resources, book reviews, and guest posts by educators who support the success of young adolescents. And be sure to subscribe to MiddleWeb SmartBrief for the latest middle grades news & commentary from around the USA.

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This is a really great article. It has helped me tremendously in making new and better decisions about homework.

Fabulous sage advice! Although I love every single suggestion you’ve included, I am particularly fond of the elimination of the “homework pass”. As a former middle-level teacher and administrator, I too found the homework pass diminished the importance of follow-up work – a necessary component in determining the level of student understanding.

I do give 2 passes, but they just extend due date by a day. And if not used, they may be returned at the end of the 9 weeks for extra credit.

Rick Wormeli’s ideas and tips in this article continue to be stimulating and useful. That said, it’s been more than a decade since the first edition of his book on grading, homework and assessment, Fair Isn’t Always Equal appeared.

In the intervening years, Rick’s thinking about homework has benefited from his work with teachers and in schools and plenty of debate. In April 2018, he published a new 2nd edition of Fair Isn’t Always Equal that includes an even deeper discussion of homework and its relationship to best practice, differentiation, and the moral obligation of educators to insist on effective homework policies.

Visitors to the Stenhouse page for the new book can preview the *entire* text for free, so be sure to check that out.

Here’s a brief excerpt from the new book:

Tenet: Homework should enable students to practice what they have already learned in class and should not present new content for the first time. Principled Responses:

• I will not assign homework to students who do not understand the content. • I will give homework to some students and no homework or different assignments to others, depending on their proficiency. • I will use exit slips and formative assessment during class so I can determine proper after-school practice for each student. • I will not give homework because parents and administrators expect me to do so, or assign homework because it’s a particular day of the week. • I will assign homework only if it furthers students’ proficiency in the field we’re studying.

Thanks to Rick for giving us permission to share this!

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Learn how two Amazon customers, Kevin and Chris are using Alexa to innovate new ways to make their families’ lives easier and their homes smarter.

How has your life changed since you started adding smart technology into your home?

Kevin: We’ve been living in our smart home for years now, and Alexa has totally changed our lifestyles. Interacting with Alexa has very much become second nature. Sometimes when we go on vacation, in the hotel, we reflexively try to speak to Alexa and are surprised when we have to walk over to the light switch. Having to do things manually feels kind of awkward these days. We have Echo devices in every room—there is an Echo Dot in every bathroom, Echo Show devices in every bedroom, Echo Auto in the car, plus about 150 other devices connected via smart plugs. One of the things we use Alexa for most frequently is to create and share shopping lists . In a large family like ours, there are always groceries to buy, and Alexa helps us keep them sorted. We wanted to be able to create one big, accessible list, no matter where we were, so whoever is at the store can pick the right things up. But beyond that, we use Alexa for so many things—to announce when it’s time for dinner, to check in on our Ring doorbells , to send messages to one another.

Chris: It’s become really natural to interact with Alexa all the time. It’s an instinctive part of our family’s daily lives now, and it’s the little things that mean the most for me. Using Alexa to control the lights in the house seems like a small job, but it’s been so incredibly useful, as has having the power to control the TV with my voice—asking Alexa to turn the volume up or to play a specific show. It likely doesn’t seem like a huge deal, but I still get joy from it every time it happens. I also love the way Alexa allows me to play music throughout the house. When it’s all bundled together, those little things add up to make life so much easier.

How did you get started with smart home technology?

Kevin: Six years ago, we had a house fire and lost everything. When the time came to build a new home, my wife and I decided to make Alexa the center of our smart home. At the time of the fire, we were using a number of manual ways to store our photos, and afterward we thought a lot about how we could find a way to safely store those precious memories so we’d always have them. Amazon Photos was kind of the catalyst, because we knew having unlimited, digital storage was priceless. From there, it only made sense to make Alexa part of our new home.

Chris: I’ve always been curious about technology. I love experimenting with it. Originally, I helped test the Blink cameras way back when they were a Kickstarter project. Over time, it made sense to add even more technology into my home. Now we’ve got a whole host of devices: multiple Echo Dot devices, an Echo Show , Fire TV sticks , and smart light bulbs, which have made my family’s daily routine much easier.

What is your favorite thing about your current smart home setup?

Kevin: One of my favorite things is the peace of mind I get from Alexa’s safety features. One of my daughters is in college and lives in our finished basement. Because of that, we sometimes miss her coming home, and of course, we worry about her. We have an Alexa-enabled garage sensor, so I can check whether she’s come home, and if we want to check in on each other, we can drop in on her via the Echo Show devices throughout the house. When she was in high school, we would use the Echo Show to make sure she’d woken up before school, but now it just gives us peace of mind that she’s at home and safe. My wife and I recently welcomed another daughter, and she will grow up with Alexa as part of her life. Sometimes we think that, given how often she will hear it, Alexa could be her first word.

Chris: Alexa has helped me solve the simplest of problems in my life. My dogs sometimes need to go out in the middle of the night. Previously, if I didn’t hear them barking overnight, I’d always wake up to morning surprises. I wanted to find a way to be notified and woken up if my dogs were barking—Alexa was the perfect solution. Now if the dogs are barking, asking to be let out, I’ll get a notification from Alexa that goes straight to my smartwatch, so I’ll wake up and go let them into the yard.

Learn more about how you can use Alexa to set up your smart home and make your life a little easier .

If you have a story of your own about how you use Alexa, we'd love to hear it, and we will continue sharing some of our favorite #AlexaStories. You can email us or tag us on Twitter or Instagram @alexa99 or #AlexaStories. Additionally, to learn more about how Alexa is helping people in different ways, you can watch Alexa Stories videos .

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The Role of AI in Smart Home Evolution

A re you curious about the latest advancements in the world of smart homes and how artificial intelligence is transforming our living spaces? Well, you’ve come to the right place. In this post, we will delve into the fascinating world of AI and explore how it is revolutionizing the way we interact with our homes. From voice-controlled assistants to smart appliances and automated security systems, AI is reshaping the way we live, making our homes more convenient, secure, and efficient than ever before.

AI (Artificial Intelligence) is revolutionizing the way we interact with our homes. It plays a crucial role in the evolution of smart homes by enhancing home security and streamlining home automation. AI enables smart home devices to learn and adapt to our preferences over time. This means that devices like smart thermostats can automatically adjust the temperature based on our habits and preferences, providing a comfortable environment without the need for manual intervention. Additionally, AI-powered voice assistants like Amazon Alexa or Google Assistant enable us to control multiple devices with just our voice, making it easier and more convenient to interact with our smart homes. AI also enables smart home devices to perform complex tasks, such as recognizing faces or distinguishing between different types of sounds, enhancing security and personalization. Overall, AI brings a new level of intelligence and automation to smart homes, making them more convenient, efficient, and personalized to our needs.

Home Security

AI is transforming home security systems, making them smarter and more efficient. With AI-powered cameras and motion sensors, homeowners can enjoy a higher level of protection and peace of mind. These advanced systems can differentiate between a potential threat and harmless activity, reducing false alarms and providing accurate notifications. AI algorithms can also learn and adapt to the homeowners’ behavior, automatically adjusting security settings based on their preferences.

Home Automation

In addition to enhancing security, AI is streamlining home automation processes. Smart homes equipped with AI can intelligently control various aspects of the household, such as lighting, temperature, and entertainment systems. Through voice commands or smartphone apps, homeowners no longer have to manually operate individual devices. AI technology enables seamless integration and synchronization, allowing users to create custom routines and schedules for their smart devices. This not only saves time and effort but also increases energy efficiency by optimizing the use of resources.

Smart Thermostats and Energy Management

The emergence of smart home technology has revolutionized the way we manage our energy consumption. One of the key components of this evolution is the introduction of smart thermostats. These innovative devices utilize artificial intelligence to learn and adapt to the household’s heating and cooling preferences. By analyzing data such as temperature patterns, occupancy, and weather forecasts, smart thermostats can optimize energy usage, saving both money and resources. With the ability to be controlled remotely through smartphone apps, homeowners can conveniently adjust the temperature settings, ensuring comfort and efficiency. Additionally, some smart thermostats can integrate with other smart devices, such as occupancy sensors and smart blinds, further enhancing energy management capabilities.

AI-powered Lighting Systems

Another exciting development in the realm of smart home technology is the advent of AI-powered lighting systems. Gone are the days of manually flipping switches; these intelligent lighting systems can automatically adjust brightness, color, and intensity based on a variety of factors. By utilizing AI algorithms, these systems learn the household’s lighting preferences and adjust accordingly. For example, they can simulate natural daylight cycles to regulate sleep patterns or create personalized lighting scenes for different activities, such as reading or entertaining. Moreover, AI-powered lighting systems can be integrated with voice assistants like Amazon Alexa or Google Assistant, allowing users to control the lights with simple voice commands. With their energy-saving capabilities and customizable features, AI-powered lighting systems are not only convenient but also environmentally friendly.

What are some examples of AI powered devices and technologies in smart homes?

AI-powered devices and technologies are revolutionizing the way we interact with our smart homes. Some popular examples include voice assistants like Amazon Echo and Google Home, which use natural language processing and machine learning to understand and respond to our commands. These smart speakers can control various aspects of our homes, such as adjusting the thermostat, turning on lights, or playing music, all through voice commands. Another example is smart thermostats like the Nest Learning Thermostat, which uses AI algorithms to learn your schedule and preferences, automatically adjusting the temperature to optimize energy efficiency and comfort. AI-powered security systems, such as the Ring Video Doorbell, use computer vision to detect and alert you about any suspicious activity around your home. These are just a few examples of how AI is enhancing our smart homes, making them more convenient, efficient, and secure.

How does AI enable smart homes to learn and adapt to the needs and preferences of the residents?

AI technology plays a crucial role in enabling smart homes to learn and adapt to the needs and preferences of the residents. By utilizing machine learning algorithms, AI systems can gather and analyze data from various sensors and devices within the home. This data provides valuable insights into the residents’ daily routines, habits, and preferences. With this information, AI algorithms are able to make intelligent predictions and recommendations to enhance the overall living experience. For example, AI can learn when the residents typically wake up and adjust the thermostat accordingly, or it can recognize patterns in their entertainment choices and suggest personalized content. Ultimately, AI enables smart homes to become more intuitive and responsive, creating a seamless and customized environment for the residents.

How is AI being used to create a seamless and personalized user experience in smart homes?

AI is being used to create a seamless and personalized user experience in smart homes by analyzing user behavior and adapting to their preferences. Smart home devices equipped with AI can learn from patterns and interactions to anticipate the user’s needs and automate tasks accordingly. For example, AI-powered voice assistants like Amazon Alexa or Google Assistant can understand and respond to natural language commands, allowing users to control various smart devices in their homes effortlessly. Additionally, AI algorithms can analyze data from sensors and cameras to detect patterns and make predictions, such as adjusting the temperature based on the user’s preferences or automatically turning on lights when someone enters a room. This level of personalization and automation enhances the user experience, making smart homes more intuitive and convenient.

Are there any limitations or challenges in implementing AI in smart homes?

Yes, there are some limitations and challenges in implementing AI in smart homes. One of the main limitations is the lack of standardization in the industry. Each smart home device or platform may use different AI technologies and protocols, making it difficult for them to communicate and work together seamlessly. Another challenge is the potential for privacy and security breaches. AI collects and processes a massive amount of data from smart home devices, which can be vulnerable to hacking or misuse. Additionally, there is a learning curve for users to understand and utilize AI-powered features effectively. Not everyone may be comfortable with the idea of AI making decisions or automating tasks in their homes, which can pose a challenge for widespread adoption. However, as technology advances and industry standards become more established, these limitations and challenges are likely to be addressed and overcome.

What is the future outlook for AI in smart home technology?

The future outlook for AI in smart home technology is extremely promising. As technology continues to advance at a rapid pace, AI is expected to play a crucial role in the evolution of smart homes. AI-powered virtual assistants like Amazon Alexa and Google Assistant have already made a significant impact by enabling users to control various aspects of their homes through voice commands. Going forward, we can expect AI to become even more integrated into our daily lives, with smart home devices becoming even smarter and more intuitive. From automated lighting and temperature control to personalized home security systems, AI will continue to enhance the convenience, comfort, and efficiency of our homes. With advancements in machine learning and data analysis, AI will also be able to anticipate our needs and preferences, further enhancing the overall smart home experience.

I hope you found the information insightful and enjoyed exploring the fascinating world of AI-powered smart homes. As technology continues to advance, the integration of AI into our everyday lives becomes increasingly prominent. It is truly exciting to witness the evolution of smart homes and the convenience and efficiency they bring to households worldwide.

The post The Role of AI in Smart Home Evolution appeared first on AllTheThings .

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Boosting Smart Home Device Security Protocol

Task: You have just joined as an IoT Architect at Ingenious IoT. The first project you have been tasked with is the setup of a demo IoT Smart Office, with a link to the company offices. The project is divided into 2 parts:

• Create a smart office with the criteria and devices given
• Control these smart devices from the branch office which is in the same city.

The Main Smart Office includes, but is not limited to the following smart devices:

• Ceiling Fan
• Front Door Lock
• Motion Detector
• Lawn Sprinkler System
• Smoke Detecto
• Temperature Monitor
• Anemometer (Wind Speed Detector)

These devices are linked to a Wireless Router, which is linked via an Ethernet cable to a Cable Modem. The modem is connected to the Internet via an ISP known as Optras. All the devices registered on the Remote Server can be controlled locally by a Tablet which is also connected to the wireless network. There is a Remote Server connected to the Company's Cloud Cluster service (run by Sky Servers), as well as an external server that the Smart office uses for backups

Ensure that you add all necessary screenshots with the documentation as well as the packet tracer file to be presented to the manager for project approval

Tasks: Build and configure a smart office. (Refer to the file on the Student Resources named Assignment_2_Help). Start the smart office from the basic file, available from the link below: https://static-course-assets.s3.amazonaws.com/I2PT/en/index.html#6.2.1.2

• Add, connect, enable and configure the registration server, tablet and wireless router as shown in the diagram below
• Add, configure the smart devices mentioned in the case study and name these as given in the diagram below. Note that these devices should be connected to a wireless router wirelessly and should be encrypted using AES by using WPA-PSK (PSK pass phrase must be only your student ID
• Connect and activate the devices with username and password as Student ID only.
• Set up all the smart device attributes through the tablet web browser for the devices to work as shown in the diagram below
• Show the protocols that are used in transmitting a simple PDU from the tablet to the Server.

Add a new branch office in the same city. Either move the same tablet to the branch office or use your smart phone and connect to the main smart office server. You can use any connecting and intermediary devices of your choice. Log on to the web browser on your tablet/smart phone and manage the devices.

U se your tablet/smart phone to:

• Turn off the Sprinkler
• Turn off the ceiling fan
• Lock the door
• Dim the lamp
• Turn on the smoke alarm
• Turn on the motion detector
• Turn on the wind detector

Rationale This assessment covers the following learning outcomes:

• be able to explain and demonstrate various components of Internet of Things (IoT);
• be able to analyse the role and importance of IoT in the modern world;
• be able to investigate and propose various requirements of IoT for real world applications

Introduction: The adoption of smart home technology is on the rise across the globe. The smart device requires to interconnect within a network so as to work effectively. While some are reliant on the internet to establish the connections others simply need to establish a connection between device to communicate and perform effectively.

The previous report has been designed based upon the smart office network and its configuration based upon the requirement of the plan. The report also contains the screenshot of the working of the management so as to get a clear picture of the IoT network and its working. The screenshot also describes the configuration of the device with the help of Wi-Fi network to all the smart home device. In order to connect the Smart home technology with a particular network it has to go through a process of configuring the system with the Wi-Fi device with certain username and password. When the configuration is done successfully, it is necessary to create a new account with the help of a tablet or a smartphone and create a new username and password so that the management of the device and be operated successfully. The tablet or the smartphone will allow to login to the established network and then all the devices can get connected to the same network. The configuration of the wireless device is very essential for establishing an efficient network connection between the device and the wireless connection. It will help in mobility and also help to transfer the device from one place to another without any physical labor. Working and connecting the device with this network is very crucial as it help both the employees and the company to work effectively with the advanced method.

1. Configuring the Smart IOT network within Packet Tracer

Figure 1: Screen shot of connecting the Smart home technology

2.I. Screenshot of the IoT device connecting and its configuration.

II. Screenshot for registering the device and creating an account

3. Screenshot for opening the Smart IOT device with the help of a smart device

4. Simple PDU from Tablet to the server

Conclusion It can be concluded from the above screenshot that all the smart home device has been configured successfully and is able to meet the requirement of the organization in all possible ways. The network is also being tested through the established connection via a smart home device and its process of registration is also being conducted to have an authentic and secured network. The Smart home technology connection is being preferred over the wired network because it helps to connect all the device easily and successfully with the use of wire. This will help to reduce the cost of wiring and even allow the device to have a flexible network that can move from one place to another without any problem. It is also necessary to note the location of the wireless device because it is very important to establish connection within a range so that all the device that are connected with the wireless network can be able to connect with each other with strong network connection. Thus, it is necessary to have to keep and router in a middle position so that it can connect all the device successfully. It must also be noted that Wi-Fi booster can also be installed in those places where connection is weak so that a better network connection can be established.

Introduction: The second part of the project deals with the branch of the same management and the organization but is situated to a new and different location. The remote branch of the office and its diagram has also been demonstrated. It must be noted that the new branch is also connected to the same network in which the head office is being registered. This will allow to control the branch office of remote location to be able to control easily from the head office and will have a clear and established connection. Thus, a cable wire has been drawn from the head office to the remote location by which the connection can be established. A switch is also being installed in order to connect and disconnect the established network. The work will be done entirely online and thus a cloud based network has been formed which is directly connected from the head office computer and this is important as the data can be transferred easily via this cloud platform. Employees will have a clear access of all the required data and can be connected easily with the network easily. It is also important to have a access point network which is important to establish connection between the device of the head office with that of the device of the remote location. The access point has been established based upon the requirement of the organization which is important to establish network connection with the smart device to that of the connecting network in order to have a clear access of all the IoT devices which has been installed in all parts of the Smart office location. Establishing the network connection is highly important because it will allow the device to connect with the home network and work efficiently.

The screenshot that has been provided will help to understand the connection of the IoT devices with the help of any smart device or application.

Packet Tracer File

Conclusion By the above scenario it can be easily stated that with the extreme growth and development of the smart IOT network which is very advanced, in some of the operations it can be done in the automated form. It can creates as well as increases the flexibility of the network by allowing the devices which is linked or merged with the network just to manage or organize from any of the location in this network. This devices basically constructed as per the requirement of the needs and wants too of the corporation and also do research which has been made on the device of IOT only and also construct for the increment of the network’s framework. This IOT device is configured to link up with the wireless access point and accurate authentication which is used especially for the maintenance of the security of the network. To test or check the connection of the network ICMP packets and give in between the sources (tablet/smart phone) and the aim (the registration server) just to decrease the problems or errors within the network and effectively delivered or supplied the project. This IOT network is very advanced in nature and due to this many problems can be sorted out easily. By this we can save a lot of time because it is very helpful in nature. This network can easily connect with the other one or with any other device which is also very advantageous by this doing of work through internet with the help of IOT can do in an easy way. The smartphones and the other devices like computer, tablets etc. are not going to have any damage like virus or any other things them. So, in other words, the devices are also get secured by the use of this IOT network.

Bibliography Behan, M. and Krejcar, O., 2013. Modern smart device-based concept of sensoric networks. EURASIP Journal on Wireless Communications and Networking, 2013(1), p.155.

Domingo, M.C., 2012. An overview of the Internet of Things for people with disabilities. Journal of Network and Computer Applications, 35(2), pp.584-596.

Kerski, J.J., 2003. The implementation and effectiveness of geographic information systems technology and methods in secondary education. Journal of Geography, 102(3), pp.128-137.

Luftman, J.N., Bullen, C.V., Liao, D., Nash, E. and Neumann, C., 2004. Managing the information technology resource: Leadership in the information age. Upper Saddle River, NJ: Pearson Education.

Mumtaz, S. and Rodriguez, J. eds., 2014. Smart device to smart device communication. Switzerland: Springer International Publishing.

Piyare, R., 2013. Internet of things: ubiquitous home control and monitoring system using android based smart phone. International Journal of Internet of Things, 2(1), pp.5-11.

Vermesan, O. and Friess, P. eds., 2013. Internet of things: converging technologies for smart environments and integrated ecosystems. River Publishers.

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'ZDNET Recommends': What exactly does it mean?

ZDNET's recommendations are based on many hours of testing, research, and comparison shopping. We gather data from the best available sources, including vendor and retailer listings as well as other relevant and independent reviews sites. And we pore over customer reviews to find out what matters to real people who already own and use the products and services we’re assessing.

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ZDNET's editorial team writes on behalf of you, our reader. Our goal is to deliver the most accurate information and the most knowledgeable advice possible in order to help you make smarter buying decisions on tech gear and a wide array of products and services. Our editors thoroughly review and fact-check every article to ensure that our content meets the highest standards. If we have made an error or published misleading information, we will correct or clarify the article. If you see inaccuracies in our content, please report the mistake via this form .

The Echo Show 8 is the best smart display and speaker combo and it's only $105

What's the deal?

The newest Echo Show 8 is on sale for $105 with a limited-time Amazon deal.

ZDNET's key takeaways

• The third-generation Echo Show 8 was launched for sale in late 2023 for $150.
• An improved speaker now boasts spatial audio, the smart display features a centered camera for improved video calls, and the new Adaptive Content trait. 
• Though it has many improvements, the screen remains at 1080p resolution, and the customization options are minimal.

The all-new Echo Show 8 was recently released by Amazon, and I've been able to test it for the past few months. Coming from a first-generation Echo Show 8 makes the upgrades to the third-generation model all the more noticeable.

Also: How a 'smart calendar' changed the way my family stays connected

Aside from featuring a sleeker design with edge-to-edge glass, a center-set built-in camera, and a smaller speaker enclosure, the 2023 Echo Show 8 features spatial audio, a faster processor than previous generations, and an upgraded smart home experience.

Amazon's updates to its third-generation Echo Show are evident from the moment you unbox the device. The typical plastic packaging is nowhere to be seen, as the device is wrapped mostly in recyclable materials. Amazon says 99% of the device's packaging is made of wood-fiber materials from "responsibly managed forests or recycled sources."

Once unboxed, the Echo Show 8 stands out from previous generations for its different design, particularly the speaker on the back. Other Echo Show devices had a tapered speaker back that doubled as a stand to hold the smart display up to make it self-standing. The latest Echo Show 8's speaker also props up the display, but it has a new design that is, in my humble opinion, an interesting choice. 

The spatial audio speaker on the backside, let's admit it, looks a bit like a butt sticking out of the smart display, especially when viewed from an angle and just a sliver of it is visible. This isn't a negative thing, it's just the unfortunate perception the device gave me.

The spatial audio speaker on the backside, let's admit it, looks a bit like a butt sticking out of the smart display, especially when viewed from an angle and just a sliver of it is visible. This isn't a negative thing, it's just the unfortunate perception the device gave me.Suboptimal backside design choices are just one of the many new features packed into the latest generation Echo Show. Amazon finally placed the device's camera in the middle of the display instead of on the right, as it had done with each Echo Show before this one. 

Also: Amazon's Echo Show 5 made me a smart display believer (and my daughter, too)

The upgraded 13MP camera is meant for video calls, making the center placement all the more beneficial to prevent people from appearing as if they're looking off to one side when on a video call.

The camera placement from the right to the center could also better support Visual ID, making it possible for the Echo Show 8 to detect when a person approaches the device. 

Visual ID personalization is available in the Echo Show 10 , Echo Show 15 , and Echo Show 8 second generation and newer. This feature makes it easier for each person in a household to have a personalized experience with the smart display. 

Also: This new tablet is redefining what a kids' tablet can do

Each household member who wants to use Visual ID has to enroll separately on each device; as Amazon explains, this visual data is stored locally on the device. When an enrolled member approaches the Echo Show 8, the screen will display personalized content, such as sticky notes someone else left for that person, calendars, and their favorite news.

The Echo Show 8 (2023) switches from Standby Mode to a more interactive screen when a registered user is detected.

But Visual ID differs from Adaptive Content, an all-new feature Amazon is debuting with this third-generation device. Adaptive Content will make it easier for consumers to view content on the screen from a distance by simplifying it when no one is near the device and switching to a detailed view when the person approaches the Echo Show.

Also: 5 things I learned while building my smart home

If the user is also enrolled in Visual ID, then the displayed content will be personalized to that user. Adaptive Content is also coming to the second-generation Echo Show 8 and other Echo Show devices as soon as early 2024. 

The latest Echo Show 8 is also a smart speaker with spatial audio. The device packs a pair of two-inch neodymium speakers for a vibrant directional feel and a surprisingly rich sound experience despite the size of the speaker, especially compared to the first-generation model and the   2023 Echo Show 5 . 

ZDNET's buying advice

The newest Echo Show 8 makes a compelling case for a smart display for both Alexa fans and smart home enthusiasts. Still, the $150 MSRP, higher than previous generations, could be a deterrent (though frequent discounts easily remediate that).

Also: Everything you need to start building a smart home

However, This latest smart display is notably faster than previous models in the screen's touch response and how quickly Alexa responds to and processes requests. 

For smart home users, the Echo Show 8 can process common smart home requests locally for 40% faster responses when you ask Alexa to turn on a smart light or smart plug. The new Echo Show 8 is also not just a smart display with Alexa built-in, but a smart home hub like the Echo and higher-end Echo Show 10. 

With support for Zigbee, Thread, and Matter , the Echo Show 8 can be a central nexus to set up and control compatible smart home devices and a smart assistant.

When will this deal expire?

Deals are subject to sell-out or expire anytime, though ZDNET remains committed to finding, sharing, and updating the best product deals for you to score the best savings. Our team of experts regularly checks in on the deals we share to ensure they are still live and obtainable. We're sorry if you've missed out on this deal, but don't fret -- we're constantly finding new chances to save and sharing them with you at  ZDNET.com .

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Govee's new smart floor lamps bring Matter support and built-in speakers to spruce up any space

Published on April 15, 2024

• Govee has announced two new smart floor lamps: the Floor Lamp 2 and the Floor Lamp Pro.
• Both these lamps offer higher brightness and more LEDs, while the Lamp Pro also comes with a built-in speaker.
• The Floor Lamp 2 is available now for $149.99, while the Floor Lamp Pro will be available from April 22 for $219.99.

Govee has updated its smart lamp lineup with the Govee Floor Lamp 2 and Govee Floor Lamp Pro. These new models add more LEDs, improved brightness, and a few innovative features.

The Floor Lamp 2 is a successor to Govee’s popular Lyra model and brings many upgrades. It packs in more LEDs with higher brightness and a taller design to effectively brighten larger spaces. The addition of Matter compatibility is a major benefit, enabling easy integration with popular smart home platforms like Google Home and Apple HomeKit for streamlined control.

Like other current-gen Govee products, the Floor Lamp 2 offers RGBICWW technology for a wide range of colors and white temperature adjustments, perfect for everything from relaxation to throwing a party. This model could be a worthy mid-range consideration for those seeking to upgrade their space with smart, adaptable lighting.

The Floor Lamp Pro is Govee’s flagship model. It features 324 strategically placed LEDs, promising a substantial 2100 lumens of brightness for consistent room-wide illumination. A key feature is the lamp’s 300-degree rotating light bar, allowing for flexible corner placement without compromising light output. For some reason, Lamp Pro’s Matter compatibility is still under wraps.

The standout feature of the Pro model is its multifunctional base, which features customizable lighting effects and a built-in Bluetooth speaker (including two full-range and one low-frequency speaker). Additionally, the Pro includes a library of 29 white noise presets with corresponding lighting effects, making it useful for activities like meditation, yoga, or focused work.

Price and availability

Both new Govee floor lamps include the brand’s established smart lighting features. You can manage them through the Govee app (available on Android and iOS), and both products offer dynamic lighting effects, scheduling options, and voice command compatibility. The Floor Lamp 2 comes in a solo metallic black finish, while the Pro model also offers a metallic gray variant.

The Govee Floor Lamp 2 is currently available for $149.99 on the official Govee website and Amazon. The Govee Floor Lamp Pro is slated for release on April 22, 2024, and will retail for $219.99 on the same platforms.

Android Police

Ecobee smart doorbell camera (wired) review: overall excellence with one limitation.

This smart doorbell would reach great heights if not tied down by its wire

A few well-established brands, like Ring and Nest, dominate the smart video doorbell territory. That’s no secret. However, that doesn’t mean there aren’t unsung heroes vying for some of the market share. Many of these contenders offer nearly identical specs without the A-list price.

Ecobee, largely known for its smart climate devices like thermostats, released its first-ever smart doorbell last October. This is the brand’s second security camera, following an indoor camera released in 2020. At a glance, the new doorbell stands toe-to-toe with big-name competitors in many aspects — but does Ecobee earn a space among the security goliaths? Let’s take a look.

Ecobee Smart Video Doorbell

With an expanded 175-degree vertical field of view and HD night vision, the Ecobee Smart Video Doorbell doesn't let anything go undetected. This doorbell not only sends an immediate activity notification, but also zooms in on and tracks the package or person via its Smart Focus technology. This is Ecobee's first doorbell, offering activity zones, a remotely activated siren, rich notifications, and package detection.

• Reliable package detection
• Dynamic activity zones
• Easy install
• Crisp two-way audio
• No battery charging hassle
• Wired doorbells aren't for everyone
• Lacks one or two premium features, like color night vision

Price, availability, and specs

The Ecobee Smart Doorbell Camera is available for $160 directly through Ecobee and just about any major electronics retailer. This includes Best Buy, Lowe’s, Office Depot, and Amazon. At the time of this review’s publication, the doorbell is on sale for $150 through Amazon.

Specifications

What’s good about the ecobee smart doorbell camera, honestly, (almost) everything.

The Ecobee Smart Doorbell Camera offers almost everything you want in a doorbell. Ecobee’s first smart wired doorbell offers fiercely competitive surveillance and a clean app experience. The housing is durable and up to withstanding the elements. The camera consistently delivers on clarity and quality, even at night. There’s no color night vision, but video and images are still crisp and legible.

The app allows for activity zones, customized alerts, two-way audio, siren activation, and rich push notifications (you need a subscription for that). The audio output on both ends was impressive and clear, although the person at the doorbell reported a few-second delay when I was speaking through the app. Like several other doorbells, the Ecobee includes a beta feature called Smart Focus, where the camera automatically locks its focus on a person in view. The lens will pan and zoom to follow the tracked person.

While the feature generally worked well, Smart Focus needs just a touch more refinement. Its panning and zooming were often too slow to keep up with a person walking through the frame.

If you’re in the market for a smart doorbell and already have an Ecobee thermostat, namely the premium version, getting the brand’s new doorbell is a no-brainer.

If you’re in the market for a smart doorbell and already have an Ecobee thermostat, namely the premium version, getting the brand’s new doorbell is a no-brainer. Ecobee nailed it in creating a harmonious comfort-security ecosystem; my Smart Thermostat Premium became a security hub for the doorbell. It offered a secondary live view, displaying a real-time stream of the doorbell’s camera when activity was detected and acted as another chime when the doorbell rang. This doorbell also plays nice with Apple HomeKits or Alexa homes; compatible speakers and devices can read out doorbell notifications, show live video, and allow two-way audio.

Package detection was the most reliable I’d ever experienced in a video doorbell, partly thanks to Ecobee’s 175% vertical field of view, which allows the user to see packages placed right up against the door. Package detection is locked behind an Ecobee Smart Security paywall, but I highly recommend utilizing this subscription with the Smart Doorbell Camera.

The two paid plan options are remarkably affordable at $5 or $10 per month, with the upper Complete plan offering professional monitoring and 30-day video storage for unlimited camera devices. Ecobee needs to roll out some exterior security cameras to really make that unlimited storage worth it, but still — $10 per month is unbeatable. There is a free option, but you miss out on most premium features.

What’s bad about the Ecobee Smart Doorbell Camera?

Tethered by its ball and chain.

The one con about this doorbell is only subjectively a con, which is that it’s a wired model. Further, Ecobee currently only offers this doorbell camera in a wired model, so customers who need or want a wireless option are out of luck for the foreseeable future. As far as hardwired doorbells go, the Ecobee was still easy to install. The app’s illustrated, step-by-step instructions made sure of that. However, by sheer design, wired doorbells are not accessible to every user, and it seems imprudent not to immediately follow up on a wired doorbell release with a wireless one.

There are a few arguments for a wired doorbell, of course —wired connections mean the new product can communicate with your home’s existing chime, and there’s no hassle with dead doorbells and battery charging. But Ecobee says its camera is hardwired “for a reliable view, no matter the weather,” which implies that users in harsher or more unpredictable climates would derive greater benefit from a wired doorbell over a battery-powered one.

Nest Doorbell (battery) review: Worthless without a subscription

I tested this doorbell in Minnesota, which boasts arguably one of the harshest climates in the continental United States, and Ecobee’s device unseated a wireless Nest doorbell for its testing. The battery in that Nest doorbell lasts three to four months, which feels like a pretty reasonable expectation regardless of climate. Plus, which device would still be online when the power goes out in the middle of a Minnesotan blizzard? Not Ecobee’s.

If a brand wants to use weather and climates to argue the case for choosing a wired security device over battery-powered alternatives, combining the two and offering wired power with a battery backup is the best possible scenario. Generally speaking, harsher climates are more likely to experience weather-related power outages, so choosing a wired solution seems illogical.

Should you buy it?

If you hate charging batteries, this is a great option.

While I highly recommend the Ecobee doorbell, I (almost painfully) can't say it is the right fit for everyone. It's that hardwired component getting in the way. It has its perks, but it's not an accessible type of installation for too many people, and this is consequentially the only thing that could make the doorbell a hard sell. In most cases, this kind of rules the Ecobee Smart Doorbell Camera out for renters. But beyond that, with easy-to-follow instructions and a user-friendly app UI, this is a great buy for beginner and advanced smart-homers alike.

While I highly recommend the Ecobee doorbell, I (almost painfully) can't say it is the right fit for everyone.

There are a few premium features we don’t see in Ecobee’s first doorbell, like facial recognition for regular visitors and color night vision, but this is trivial with the clear infrared night video and geofencing tech that allows Ecobee households to know when a household member is home. And Ecoee doesn't currently support Google Home integration, but that's "coming soon," the brand says.

Maybe we will see a wireless version of this doorbell in the future or a “Pro” generation that introduces those premium features that will make the Ecobee more competitive with the latest from big names (like the Ring Battery Doorbell Pro ), but this is still a well-rounded and feature-rich option, especially in the under-$200 price point. It’s easy to install, performs consistently, and seamlessly integrates with Ecobee, Alexa, or Apple smart homes.

With an expanded 175-degree vertical field of view and HD night vision, the Ecobee Smart Video Doorbell doesn't let anything go undetected. This doorbell sends an immediate activity notification, zooms in, and tracks the package or person via its Smart Focus technology. This is Ecobee's first doorbell and a wired model, offering activity zones, a remotely activated siren, rich notifications, and package detection.

Best smart doorbells that don't need wired power in 2024

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The Guardian view on smart motorways: not so clever without a hard shoulder

The latest call by the RAC to reinstate emergency lanes should be listened to and acted upon

Ten years ago this week on the M25, Britain’s first stretch of all-lane running (ALR) “smart” motorway was introduced , with more to follow. Envisaged as a way to ease congestion without spending money on widening roads, ALR motorways function without a hard shoulder for drivers in difficulty. As they were rolled out, motorists were assured that the emergency lane would not be missed, as new technologies would be able to respond to breakdowns, and control traffic flow.

The public was understandably sceptical about how smart this idea was, and it turned out the public was right. Smart motorways without a hard shoulder have been found to be three times more dangerous than ones where drivers have that option. Behind the data lie horrific incidents, in which stationary vehicles have been ploughed into from behind with fatal consequences. In one tragic case , a passenger in a car which stopped to lend assistance to another vehicle was killed when a lorry crashed into it.

Rightly, Rishi Sunak last year pulled the plug on plans for more ALR motorways, citing a lack of public confidence. But he should go further. The RAC this week called for a hard shoulder to be reinstated on all smart motorways. That advice should be listened to in Whitehall and acted upon. A 2021 House of Commons transport committee report made shockingly clear that before rolling the “no emergency lane” policy out, ministers driven by the desire to save money failed to do due diligence on the safety risks attached. Since then, plans to retrofit refuge areas along motorway routes have proceeded at a snail’s pace. Of the 150 emergency areas due to be in place by next year, only a tiny proportion have been delivered . Even when all are up and running, the risk of a catastrophic breakdown in onrushing traffic will remain.

At a minimum, the government should move to a system of “dynamic” motorways, where emergency lanes are only opened to traffic at particularly busy periods. But the preferable solution would be to reinstate a permanent hard shoulder. This could be done while retaining the benefits of investment in technical innovations that allow traffic to be better monitored and flexibly regulated. As the president of the AA, Edmund King, has noted , “controlled motorways” of this kind are the safest option. That this is the most important consideration should not need stressing. Any concerns over cost should be set against the huge amounts of money already spent trying to rectify a botched policy.

For governments of any political stripe, a degree of overselling and Panglossian spin accompanies the rollout of a new initiative. But the refusal of governments to recognise that there was a good reason why ALR smart motorways were so unpopular has been reprehensible. Mr King has described lobbying 13 transport secretaries and ministers of state on the subject, only to be told he was being too “emotional” about the issue. Mr Sunak did the right thing in bringing a foolish experiment to an end. The next task is to rectify the damage already done.

• Road transport
• Road safety
• Transport policy

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