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In this work, we view smart home from 3 different sides: devices, platforms, and apps. We first attempt to better understand the problems on each side and later explore new methods and techniques to better guarantee security, privacy, and safety of smart homes. On the devices side, we discovered that smart home devices are vulnerable to passive inference attacks based on network traffic, even in the presence of encryption. We first present this passive inference attack and our techniques that we developed to exploit this vulnerability on smart home devices. We created PingPong, a tool that can automatically extract packet-level signatures for device events (e.g., light bulb turning ON/OFF) from network traffic. We evaluated PingPong on popular smart home devices ranging from smart plugs and thermostats to cameras, voice-activated devices, and smart TVs. We were able to: (1) automatically extract previously unknown signatures that consist of simple sequences of packet lengths and directions; (2) use those signatures to detect the devices or specific events with an average recall of more than 97%; (3) show that the signatures are unique among hundreds of millions of packets of real world network traffic; (4) show that our methodology is also applicable to publicly available datasets; and (5) demonstrate its robustness in different settings: events triggered by local and remote smartphones, as well as by home-automation systems. Furthermore, we also present our discussion and evaluation on existing techniques (e.g., packet padding) as possible defenses against passive inference attacks and their analyses.On the platforms side, smart home platforms such as SmartThings enable homeowners to manage devices in sophisticated ways to save energy, improve security, and provide conveniences. Unfortunately, we discovered that smart home platforms contain vulnerabilities, potentially impacting home security and privacy. Aside from the traditional defense techniques to enhance the security and privacy of smart home devices, we also created Vigilia, a system that shrinks the attack surface of smart home IoT systems by restricting the network access of devices. As existing smart home systems are closed, we have created an open implementation of a similar programming and configuration model in Vigilia and extended the execution environment to maximally restrict communications by instantiating device-based network permissions. We have implemented and compared Vigilia with forefront IoT-defense systems; our results demonstrate that Vigilia outperforms these systems and incurs negligible overhead.On the apps side, smart home platforms allow developers to write apps to make smart home devices work together to accomplish tasks, e.g., home security and energy conservation—smart home devices provide the convenience of remotely controlling and automating home appliances. A smart home app typically implements narrow functionality and thus to fully implement desired functionality homeowners may need to install multiple apps. These different apps can conflict with each other and these conflicts can result in undesired actions such as locking the door during a fire. We study conflicts between apps on Samsung SmartThings, the most popular platform for developing and deploying smart home IoT devices. By collecting and studying 198 official and 69 third-party apps, we found significant app conflicts in 3 categories: (1) close to 60% of app pairs that access the same device, (2) more than 90% of app pairs with physical interactions, and (3) around 11% of app pairs that access the same global variable. Our results suggest that the problem of conflicts between smart home apps is serious and can create potential safety risks. We then developed an automatic conflict detection tool that uses model checking to automatically detect up to 96% of the conflicts.

The rapid development of smart home and deep learning attract many types of research to meet the needs of the homeowner that make the right decisions based on preset settings or based on user behavior in the smart home. And because some gases cause air pollution, affect human health, cause lung cancer, breathing problems. and cause a fire as well. Many gases in our home can pose many dangers. One of the risks of using LPG is leakage in cylinders, gas installations, stoves, or LPG heaters. This paper focused on using technological development to alert about a gas leak inside the home to preserve the general health of people. The proposed system, a gas sensor is used to measure the value of gas in the atmosphere. This part is an important part of the smart home, which is taking precautionary measures by using the gas sensor and controller, in addition to using a mobile network system to send an alert message to the user about gas leakage from the mentioned sources. The proposed system is characterized by its low cost and allows the homeowner to monitor the gas leak inside the home from outside.

International audience; Older adults often need some level of assistance in performing daily living activities. Even though these activities are common to the vast majority of individuals (e.g., eating, bathing, dressing), the way they are performed varies across individuals. Supporting older people in performing their everyday activities is a major avenue of research in smart homes. However, because of its early stage, this line of work has paid little attention on customizing assistive computing support with respect to the specific needs of each older adult towards improving its effectiveness and acceptability. We propose a tool-based approach to allowing caregivers to define services in the area of home daily living, leveraging their knowledge and expertise on the older adult they care for. This approach consists of two stages: 1) a wizard allows caregivers to define an assistive service, which supports aspects of a daily activity that are specific to an older adult; 2) the wizard-generated service is uploaded in an existing smart home platform and interpreted by a dedicated component, carrying out the caregiver-defined service. Our approach has been implemented. Our wizard has been successfully used to define existing manually-programmed, activity-supporting services. The resulting services have been deployed and executed by an existing assisted living platform deployed in the home of community-dwelling individuals. They have been shown to be equivalent to their manually-programmed counterparts. We also conducted an ergonomics study involving five occupational therapists, who tested our wizard with clinical vignettes describing fictitious patients. Participants were able to successfully define services while revealing an ease of use of our wizard.

Home Energy Management Systems (HEMS) are of great importance today and have attracted a great deal of interest from both, academic researchers, and industrial engineers. These systems are considered as the intersection point between the smart grid and the smart home. Indeed, they can communicate with the user, home appliances, on-site generation sources, storage devices, and the grid operator to make dynamic decisions enabling intelligent and efficient energy management. This work aims to design and implement a smart HEMS that ensures renewable energy integration and energy efficiency improvement in residential buildings. The system designed in this work has been implemented as part of a pilot project in a testbed house of the Smart Campus — Green & Smart Building Park, Benguerir, Morocco. The proposed HEMS framework is based on two control strategies that work jointly: the first concerns the scheduling and control of power dispatch among generation, consumption, and storage agents (Supply-Side Management), while the second concerns the scheduling and control of flexible appliances for optimal load profile modulation (Demand-Side Management). The management of energy flows is based on grid electricity price, forecasting data (PV generation and weather conditions) and user preferences. The two designed control strategies are combined into an AI-based multi-objective optimization algorithm that minimizes costs and maximizes comfort level simultaneously. The obtained results validate the effectiveness of the proposed algorithm and indicate that it would significantly increase the penetration of PV energy for self-consumption and reduce electricity costs, while ensuring a proper compromise between monetary spending and comfort level. The large-scale implementation of such systems would help decarbonize residential energy sector through higher renewable energy integration and energy efficiency improvement in buildings.

Smart homes of the future will have to deal with multi-occupancy scenarios. Multi-occupancy systems entail a preliminary and critical feature: the capability of counting people. This can be fulfilled by means of simple binary sensors, cheaper and more privacy preserving than other sensors, such as cameras. However, it is currently unclear how many people can be counted in a smart home, given the set of available sensors. In this paper, we propose a graph-based technique that allows to map a smart home to an undirected graph G and discover the lower-bound of certainly countable people, also defined as certain count. We prove that every independent set of n vertices of an undirected graph G represents a minimum count of n people. We also prove that the maximum number of certainly countable people corresponds to the maximum independent sets of G, and that the maximal independent sets of G provide every combination of active sensors that ensure different minimum count. Last, we show how to use this technique to identify and optimise suboptimal deployment of sensors, so that the assumptions can be tightened and the theoretical lower-bound improved., Funding agency:Örebro University

In this work, we present a control system for a mini electrical fan. The aim of this work is to design a controller prototype for an electrical fan within the smart home system. The system consists of an STM32L100 microcontroller for controlling the electrical fan using pulse-width modulation (PWM), TIP 102 BJT transistor for ‘off,’ and ‘on’ control of the fan, and 1N4007 fly-back diode. The PWM is generated by the STM32L100 microcontroller to control the Fan’s speed softly (25%, 50%, 75%, and 0% - 100% of speed). In addition, the system is equipped with a ZigBee module to support wireless communication with the host, which is required to receive and process commands from the Android application used for the user interface. Moreover, with the Zigbee module, the fan device enables be integrated wirelessly with other end devices in a smart home environment through a network (e.g., Mesh topology). Based on the testing performed, the system can work as intended, it can be controlled easily using Android smartphone and its speed, with the currents of 43.1 mA (idle mode) and 145.1 mA (processing mode) powered by 12 VDC.

[EN] 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., This work has been partially supported by the "Ministerio de Economia y Competitividad" in the "Programa Estatal de Fomento de la Investigacion Cientifica y Tecnica de Excelencia, Subprograma Estatal de Generacion de Conocimiento" within the project under Grant TIN2017-84802-C2-1-P. This work has also been partially founded by the Universitat Polite`cnica de Vale`ncia through the postdoctoral PAID-10-20 program.

We live in the world of digital technologies – everyone has a digital phone, television has switched to a digital broadcasting format as it is more noise-immune, digital processes are literally in every household appliance, from the iron to the computer on which this article was written. Digital technologies simplify our lives, some operations performed by humans require large material costs, for example, writing text on a typewriter and computer vary greatly. The gain of the computer is especially evident when editing the test. They brought us comfort – how nice it is when the processor that monitors the temperature in the house increased the heat supply during a cold snap or reduced it in order to save money in the absence of people in the house.But in order for the digital system to perform this or that action, it needs a command coming from the sensor. It can be a temperature, humidity, pressure sensor. Or maybe a microphone used in voice control systems. All these sensors, without which the operation of a digital system is impossible, give an analog signal that changes its value over time. The digital system is not sensitive to such a signal. It “does not understand” the signal. The problem is solved by ADC (analog-to-digital converters). They have a different structure, varying degrees of complexity, the device, depending on the parameters of the digitized signal. For example, the temperature in the house changes very slowly, even when warming up or when the heating is turned off in frost, the temperature rises or, accordingly, does not fall faster than one degree per hour. The ADC speed requirements for the temperature sensor are very low. Instead, a microphone is used to receive voice commands. In order to distinguish the voices of people and to carry out the commands of some people and not to carry out commands from others, processing of the signal spectrum with a width of kilohertz is required - which means that the signal level will change at a frequency of thousands of times per second. This is a very high demand. The different types of ADCs, their design and application will be discussed in this article.

Continual learning is an emerging research challenge in human activity recognition (HAR). As an increasing number of HAR applications are deployed in real-world environments, it is important and essential to extend the activity model to adapt to the change in people’s activity routine. Otherwise, HAR applications can become obsolete and fail to deliver activity-aware services. The existing research in HAR has focused on detecting abnormal sensor events or new activities, however, extending the activity model is currently under-explored. To directly tackle this challenge, we build on the recent advance in the area of lifelong machine learning and design a continual activity recognition system, called HAR-GAN , to grow the activity model over time. HAR-GAN does not require a prior knowledge on what new activity classes might be and it does not require to store historical data by leveraging the use of Generative Adversarial Networks (GAN) to generate sensor data on the previously learned activities. We have evaluated HAR-GAN on four third-party, public datasets collected on binary sensors and accelerometers. Our extensive empirical results demonstrate the effectiveness of HAR-GAN in continual activity recognition and shed insight on the future challenges.

Mobile edge computing which provides computing capabilities at the edge of the radio access network can help smart home reduce response time. However, the limited computing capacity of edge servers is the bottlenecks for the development of edge computing. We integrate cloud computing and edge computing in the Internet of Things to expand the capabilities. Nevertheless, the cost of leasing computing resources has been seldom considered. In this paper, we study the joint transmission power and resource allocation to minimize the users’ overhead which is measured by the sum of energy consumption and cost leasing servers. We formulate the problem as a Mixed Integer Linear Programming which is NP-hard and present the Branch-and-Bound to solve it. Due to high complexity, a learning method is proposed to accelerate the algorithm. The branching policy can be learned to reduce the time-cost of the Branch-and-Bound algorithm. Simulation results show our approach can improve the Branch-and-Bound efficiency and performs closely to the traditional branching scheme.

Residential energy trading systems (RETS) enable homeowners with distributed energy resources (DERs) to participate in virtualized energy markets that have the potential to reduce the peak demand of residential communities. Blockchains are key enablers of RETS, by virtue of providing a decentralized, self-governed network that mitigates concerns regarding privacy and transparency. However, more real-world case studies are needed to evaluate the techno-economic viability of blockchain-based RETS to improve their positive uptake. Thus, this article develops a permissioned blockchain-based RETS, which enables homeowners to select bidding strategies that consider the individual preferences of their DERs, and further evaluates the impact of the bidding strategies on reducing the peak demand of the community. The proposed system is implemented on the permissioned Hyperledger Fabric platform, where a decentralized ledger is used to store all energy bids, and a smart contract is used to execute a double auction mechanism and dispatch the homeowner DERs. The proposed system is validated by conducting simulations on a 8-home community using real-world data, and also by deploying the system to a Canadian microgrid, where the smart contract execution time is benchmarked. Simulation results demonstrate the efficacy of the proposed system by achieving a peak demand reduction of up to 48 kW (62%), which leads to an average savings of $1.02 M for the distribution system operator by avoiding transformer upgrades. Also, the simulation results show that the execution time of the proposed smart contract is 17.12 seconds across 12 nodes, which is sufficient for RETS.

The advances in the Internet of Things (IoT) provide several chances to develop a variety of innovations supporting smart home users in several industries including healthcare, energy management, etc. Ubiquitous support by intelligent appliances at modern homes, which constantly work to gather information can help us to solve everyday issues. In this article, we present a comparative study of recent advances in smart home development. The study aims to present the main trends in this field. During the analysis of the research reports and patents, we identify the propositions that constitute the main research streams. Through extensive analysis, we provide an outlook on the wide spectrum of the proposed solutions. We also analyze the main market to present which publishers are leading with the innovative science in this field. We also show the leaders of science and technology in the World. Finally, we define the ratio of the developments and outline the next stage of the development in the smart home industry.

Appliance load monitoring in smart homes has been gaining importance due to its significant advantages in achieving an energy efficient smart grid. The methods to manage such processes can be classified into hardware-based methods, including intrusive load monitoring (ILM) and software-based methods referring to non-intrusive load monitoring (NILM). ILM is based on low-end meter devices attached to home appliances in opposition to NILM techniques, where only a single point of sensing is needed. Although ILM solutions can be relatively expensive, they provide higher efficiency and reliability than NILMs. Moreover, future solutions are expected to be hybrid, combining the benefits of NILM along with individual power measurement by smart plugs and smart appliances. This paper proposes a novel ILM approach for load monitoring that aims to develop an activity recognition system based on IoT architecture. The proposed IoT architecture consists of the appliances layer, perception layer, communication network layer, middleware layer, and application layer. The main function of the appliance recognition module is to label sensor data and allow the implementation of different home applications. Three different classifier models are tested using real data from the UK-DALE dataset: feed-forward neural network (FFNN), long short-term memory (LSTM), and support vector machine (SVM). The developed activities of daily living (ADL) algorithm maps each ADL to a set of criteria depending on the appliance used. The features are extracted according to the consumption in Watt-hours and the times where they are switched on. In the FFNN and the LSTM networks, the accuracy is above 0.9 while around 0.8 for the SVM network. Other experiments are performed to evaluate the classifier model using a new test set. A sensitivity analysis is also carried out to study the impact of the group size on the classifier accuracy.

UIDB/00066/2020 The emergence of the Internet of Things concept has provided a great vision for the technological future, intending to enable the extraction and comprehension of information from the environment around us, making use of the interaction and cooperation between several technological devices. The example of Smart Homes, in particular, aims to integrate these devices into households, enabling the automation of tasks previously performed by humans, to simplify their daily lives and create a more comfortable environment. However, many of these devices fail to keep their promise, since they were not developed taking into account the frequent change of habits and tastes of the user, being necessary reprogramming of the device to follow the new behaviors. Taking this problem into account, this article presents the design and end-to-end implementation of a voice-activated smart home controller for intelligent devices, deployed in a real environment and validated in an experimental setup of motorized blinds. The architecture of the proposed solution integrates evolvable intelligence with the use of an Online Learning framework, enabling it to automatically adapt to the user's habits and behavioral patterns. The results obtained from the various evaluation tests provide a validation of the operation and usefulness of the developed system. The main contributions of this work are: I) design of a smart home controller's architecture; II) end-to-end implementation of a smart home controller and respective guidelines; III) open-source dataset of user behavior from the smart blinds scenario; IV) comparison between Online and Offline Learning approaches. publishersversion published

Hand-gesture based control has enormous potential both theoretically and for practical applications due to its convenience and intuitiveness. This study presents a real-time interactive control system for household appliances. The interactive control system allowing wireless control of household appliances using a combination of 11 hand gestures and 2 waving motions is tested on hundreds of samples. It is implemented using a regular personal computer (PC) and existing digital single processing (DSP) platforms. The evaluation results show that the system performs efficiently reaching an accuracy recognition rate of 91% and spending around 30 seconds to complete the control operation for household appliances. The contributions of this work are both academic (1) successful demonstration of the integration of algorithms for solving image detection, processing, and pattern recognition, and practical (2) showing its feasibility and using commonly available hardware and software configurations for practical uses, and finally (3) establishing a mechanism for intuitively interactive control system that facilitates smart living.

Enhancing distribution system resilience is a new challenge for researchers. Supplying distribution loads, especially the residential customers and high-priority loads after disasters, is vital for this purpose. In this paper, the internal combustion engine (ICE) vehicles are firstly introduced as valuable backup energy sources in the aftermath of disasters and the use of this technology is explained. Then, the improvement of distribution system resilience is investigated through supplying smart residential customers and injecting extra power to the main grid. In this method, it is assumed that the infrastructure of distribution system is partially damaged (common cases) and it can be restored in less than one day. The extra power of residential customer can be delivered to other loads. A novel formulation for increasing the injected power of the smart home to the main grid using ICE vehicles is proposed. Moreover, the maximum backup duration in case of extensive damages in the distribution system is calculated for some commercial ICE vehicles. In this case, the smart home cannot deliver extra energy to the main grid because of its survivability. Simulation results demonstrate the effectiveness of the proposed method for increasing backup power during power outages. It is also shown that ICE vehicles can supply residential customers for a reasonable amount of time during a power outage.

With the widespread application of smart home systems, the optimal design of smart home systems has received considerable research attention. This paper puts forward a network smart home system design scheme based on the analysis of the indoor environment and the forecast of the future indoor environment. By building a multi-level network model, an integrated model system from analysis, prediction to decision-making is formed. The swarm intelligent decision-making ability of the networked smart home system is realized by applying a recurrent neural network and a reinforcement learning method. Meanwhile, the indoor simulation environment is built, the indoor environment variables are simulated and the performance of the system is verified by the simulation environment. The simulation results show that the networked smart home system has advantages over the single smart home equipment in the performance of indoor comfort improvement.

Intelligent voice assistants are internet-connected devices, which listen to their environment and react to spoken user commands in order to retrieve information from the internet, control appliances in the household, or notify the user of incoming messages, reminders, and the like. With their increasing ubiquity in smart homes, their application seems only limited by the imagination of developers, who connect these off-the-shelf devices to existing apps, online services, or appliances. However, since their inherent nature is to observe the user in their home, their ubiquity also raises concern of security and user privacy. To justify the trust placed into the devices, the devices must be secure from unauthorized access and the back-end infrastructure tasked with speech-to-text analysis, command interpretation, and connection to other services and appliances must maintain confidentiality of data. To investigate existing possible vulnerabilities, approaches to mitigate them, as well as general considerations in this emerging field, we supplement the findings of a recent study with results from a systematic literature review. We were able to compile a list of six main types of user privacy vulnerabilities, partially confirming previous findings, but also finding additional issues. We discuss these vulnerabilities, their associated attack vectors, and possible mitigations users can take to protect themselves.

Ambient assisted living and smart home technologies are a good way to take care of dependent people whose number will increase in the future. They allow the discovery and the recognition of human’s activities of daily living (ADLs) in order to take care of people by keeping them in their home. In order to consider the human behavior nondeterminism, probabilistic approaches are used despite difficulties encountered in model generation and probabilistic indicators computing. In this article, a global method based on probabilistic finite-state automata and the definition of the normalized likelihood and perplexity is proposed to manage ADLs discovery and recognition. In order to reduce the computational complexity, some results about a simplified normalized likelihood computation are proved. A real case study showing the efficiency of the proposed method is discussed. Note to Practitioners —This article is motivated by the problem of the automatic recognition of activities that are daily performed by elderly or disabled people in a smart dwelling. The set of activities to be recognized is defined by a medical staff (e.g., to prepare meal, to do housework, to take leisure, etc.) and correspond to pathologies that have to be monitored by doctors (e.g., loss of memory, loss of mobility, etc.). The proposed method is based on a systematic procedure of offline construction of a model for each activity to be monitored (the activity discovering step). The online recognition of activities actually performed (the activity recognition step) is afterward based on these models of activities. Since the human behavior is nondeterministic, and may even be irrational, probabilistic activity models are built from a learning database. In the same way, probabilistic indicators are used for determining online the most probable activities actually performed. The efficiency of the proposed approach is illustrated through a case study performed in a smart living lab.

Energy consumption is one factor of risk in the medium term around of the world, that can be minimized by efficient use of electrical devices include its disconnection after use. This article presents a system focused on smart homes, where the concepts of Internet of Things and Artificial Intelligence are applied in the designing of a system that allows a user from a web application to disconnect and connect an electrical network in a node. From the power of the device, time of use and consumption of this, an artificial neural network was designed and trained with the backpropagation algorithm, to discriminate between seven categories (fridge, TV, iron, dryer, lamp, computer and washing machine). A percentage of accuracy of 98.914% was obtained in the training of the network and, thanks to the feedback of the user in the web application, 99.369 and 99.174% were obtained in two cases of retraining of the network.

All aspects of daily life increasingly include digitization. So-called „smart home“ technologies, as well as „wearables“, are gaining attention from more and more dwellers. Therefore, sensor-based, individualized, AI-based services for improved post-intervention monitoring and therapy accompaniment will become feasible and possible if these systems offer a related context-awareness. This paper provides an approach on how to sense and interpret specific contexts with the help of wearables, smartwatches, smart home sensors, and emotion detection software.

This paper proposes a specific domotic sensor network to measure the well-being of elderly people in private home environments through Machine Learning (ML) algorithms trained with daily surveys. The tests have been conducted in 5 apartments lived by 8 older people where the non-obtrusive sensor network is installed. Two ML algorithms are compared, Random Forest (RF) and Regression Tree (RT), such that to verify whether the users’ well-being is encoded in behavioural patterns obtained from the domotic data. These data are used to measure users’ well-being and compared with three reference indices obtained through a daily survey: a physical (Phy), a mental (Mind) and a general health index (Avg). The extracted indices from the daily survey are used to train ML algorithms in the estimation of user’s well-being for users that live alone (single-resident) or with others (multi-resident). Single-house and multi-house procedures are tested, both to extract a user-specific behaviour, and assess whether the model is able to generalise across different users and environments. Results show that the RF algorithm provides better performance than the RT algorithm in predicting the level of well-being with a Mean Absolute Error in the multi-house procedure of 32%, 13% and 17% for the Avg, Mind and Phy indices, respectively.

Recognition of activities, such as preparing meal or watching TV, performed by a smart home resident, can promote the independent living of elderly in a safe and comfortable environment of their own homes, for an extended period of time. Different activities performed at the same location have commonalities resulting in less inter-class variations; while the same activity performed multiple times, or by multiple residents, varies in its execution resulting in high intra-class variations. We propose a Local Feature Weighting approach (LFW) that assigns weights based on both inter-class and intra-class importance of a feature in an activity. Multiple sensors are deployed at different locations in a smart home to gather information. We exploit the obtained information, such as frequency and duration of activation of sensors, and the total sensors in an activity for feature weighting. The weights for the same features vary among activities, since a feature may have more importance for one activity but less for the other. For the classification, we exploit the two variants of K-Nearest Neighbors (KNN): Evidence Theoretic KNN (ETKNN) and Fuzzy KNN (FKNN). The evaluation of the proposed approach on three datasets, from CASAS smart home project, demonstrates its ability in the correct recognition of activities compared to the existing approaches.

The recent advancement and development of computer electronic devices has led to the adoption of smart home sensing systems, stimulating the demand for associated products and services. Accordingly, the increasingly large amount of data calls the machine learning (ML) field for automatic recognition of human behaviour. In this work, different deep learning (DL) models that learn to classify human activities were proposed. In particular, the long short-term memory (LSTM) was applied for modelling spatio-temporal sequences acquired by smart home sensors. Experimental results performed on the Center for Advanced Studies in Adaptive Systems datasets show that the proposed LSTM-based approaches outperform existing DL and ML methods, giving superior results compared to the existing literature.

Smart home technology, an application of Internetof Things (IoT), provides households with e.g., comfort, controland convenience. The technology has been around for sometimes,but its prevalence is not yet widespread, and thus the potentialshave largely been underestimated. The purpose of this paper is todevelop a comprehensive model that can explain a largepercentage of variance in the intention to use smart hometechnology.

Fog Computing is the distributed computing layer that lies between the user and the cloud. A successful fog architecture reduces delay or latency and increases efficiency. This paper describes the development and implementation of a distributed computing architecture applied to an automation environment that uses Fog Computing as an intermediary with the cloud computing layer. This study used a Raspberry Pi V3 board connected to end control elements such as servomotors and relays, indicators and thermal sensors. All is controlled by an automation framework that receives orders from Siri and executes them through predetermined instructions. The cloud connection benefits from a reduced amount of data transmission, because it only receives relevant information for analysis.

There is currently an era of Internet of Things in the computer systems, which consists in connecting all possible devices to the Internet in order to provide them with new functionalities and thus { to improve the user's life standard. One of such solutions could be Smart Home. The possibility of monitoring inner environment is required for such solutions. Such monitoring provides potential for e.g. better heating control. The authors of this paper propose some heating control method with Fuzzy Logic. The proposed method was tested in a special climate chamber. The authors provided conclusions at the end of the paper.

The Internet of Things (IoT) is becoming an indispensable part of the actual Internet and continues to extend deeper into the daily lives of people, offering distributed and critical services. Mobile agents are widely used in the context of IoT and due to the possibility of transmitting their execution status from one device to another in an IoT network, they offer many advantages such as reducing network load, encapsulating protocols, exceeding network latency, etc. Also, Blockchain Technology is growing rapidly allowing for the addition of an approved security layer in many areas. Security issues related to mobile agent migration can be resolved with the use of Blockchain. This paper aims to demonstrate how Blockchain Technology can be used to secure mobile agents in the context of the IoT using Ethereum and a Smart Contract. The transactions within the Blockchain are used to detect the malevolent mobile agents that could infiltrate the IoT systems. The proposed model aims to provide a secure migration of mobile agents to ensure security and protect the IoT applications against malevolent agents. The case of a smart home with multiple applications is applied to verify the proposed solution. The model presented in this paper could be extended to a wider selection of IoT systems outside of the smart home.

Existing work in energy demand side management focuses on the interaction between the utility grid and consumers. However, the previous technique is not focused on energy trading in local community of a renewable energy generation, distributed demand side management and not suitable for real-time environment. This paper presents a distributed demand side management system among multiple homes in community microgrid, with the integration of the internet of things smart meter and in the presence of renewable energy sources. The proposed energy consumption game is formulated for minimizing the cost of electricity in the individual home and the total cost of energy consumption in the whole community. The smart home users are playing game by optimizing their own daily energy consumption of appliances. The multiple participants include the self renewable generation of users, shared community microgrid and optional utility company. Each participant applies its best strategy to minimize energy consumption cost and users can maintain their own privacy of energy consumption. Moreover, the proposed scheme is distributed on blockchain, which provides a trusted communication medium between the participants. It enforces the autonomous monitoring of smart appliances and the billing of electricity consumption via smart contracts. Solidity smart contract is deployed to facilitate the execution of transactions without the involvement of third party in the smart community. Comparison of the results show that the proposed approach minimizes the total cost of energy consumption as well as each user's energy consumption cost.

Residential power scheduling for demand response in a smart grid is a complex task. The traditional methods aim to minimize consumption costs and maximize consumption payoffs. In this paper, a power scheduling algorithm based on cost efficiency for smart homes is proposed to improve consumers’ consumption efficiency and satisfaction. In this proposed method, a definition of cost efficiency for residential power scheduling is introduced. Consumers’ consumption costs are modelled based on electricity payments and users’ discomfort. A pair of parameters for a trade-off between users’ discomfort and their electricity payment is designed to model cost efficiency based on the consumer’s preference. A power scheduling algorithm based on cost efficiency is developed by adopting a fractional programming approach. Four consumption models are analysed and discussed. The results show that this proposed method can effectively improve consumers’ consumption efficiency and satisfaction while saving costs. It is shown that discomfort and additional payment can impact consumers’ consumption behaviour and smooth their consumption profile curves.

Activity recognition benefits the lives of residents in a smart home on a daily basis. One of the aims of this technology is to achieve good performance in activity recognition. The extraction and selection of the daily activity feature have a significant effect on this performance. However, commonly used extraction of daily activity features have limited the performance of daily activity recognition. Based on the nature of the time series of sensor events caused by daily activities, this paper presents a novel extraction approach for daily activity feature. First, time tuples are extracted from sensor events to form a time series. Subsequently, several common statistic formulas are proposed to form the space of daily activity features. Finally, a feature selection algorithm is employed to generate final daily activity features. To evaluate the proposed approach, two distinct datasets are adopted for activity recognition based on four different classifiers. The results of the experiment reveal that the proposed approach is an improvement over the commonly used approach.

Two-way communication facilities and advanced metering infra-structure enable residential buildings to be capable of actively participating in demand side management schemes. This paper proposes a new home energy management system (HEMS), which optimally schedules the operation of home energy resources, with the aim to minimize the home’s one-day electricity cost charged by the real-time pricing while taking into account the monthly basis peak power consumption penalty, charged by the demand charge tariff. To better ensure the user’s lifestyle requirements, the HEMS also models lifestyle-related operational dependencies of household appliances. Numerical simulations and case studies are conducted to validate the reasonability of the proposed method. Australian Research Council

This paper presents one approach to improve smart home user experience, by providing location-based services. Within the cloud of the existing smart home solution, the geolocation micro-service is first implemented. This micro-service communicates with third party online geolocation service to obtain latitude and longitude values from physical address and vice versa. The geolocation micro-service is used by different services to provide location-based experience. For example, weather micro-service uses the geolocation information, to provide weather content, while the traffic service monitors traffic conditions in the vicinity of the household. This information can be used to setup different automation rules in the system.

I-energy and its applications used in the smart grid are chosen as attractive research issues due to their widespread applications in the smart home. In this vein, the optimization of energy demand in smart home appliances, in a smart grid, is considered the main challenge faced by power companies during peak periods regarding its considerable effect on the global power system stabilities. Therefore, this paper aims to show explicitly the performance of smart grid on electricity and hydrogen applications with reused communication methods. To facilitate the understanding of the communication-reused methods in the smart grid, several components are identified and presented in terms of improvements, benefits and lack of features. In the smart grid, most innovation communications are being used as wireline and wireless bundle, where the main features are aggregated. Indeed, reducing consumers’ electricity bills in smart home electrical systems relies on increasing the relationship between the highest energy demand and annual energy use. The most appropriate research has been reviewed on optimization approaches discussing the smart home electric system. The examined methods are categorized into trustworthy and metaheuristic algorithms.

As energy demand continues to increase, demand response (DR) programs in the electricity distribution grid are gaining momentum and their adoption is set to grow gradually over the years ahead. Demand response schemes seek to incentivise consumers to use green energy and reduce their electricity usage during peak periods which helps support grid balancing of supply-demand and generate revenue by selling surplus of energy back to the grid. This paper proposes an effective energy management system for residential demand response using Reinforcement Learning (RL) and Fuzzy Reasoning (FR). RL is considered as a model-free control strategy which learns from the interaction with its environment by performing actions and evaluating the results. The proposed algorithm considers human preference by directly integrating user feedback into its control logic using fuzzy reasoning as reward functions. Q-learning, a RL strategy based on a reward mechanism, is used to make optimal decisions to schedule the operation of smart home appliances by shifting controllable appliances from peak periods, when electricity prices are high, to off-peak hours, when electricity prices are lower without affecting the customer's preferences. The proposed approach works with a single agent to control 14 household appliances and uses a reduced number of state-action pairs and fuzzy logic for rewards functions to evaluate an action taken for a certain state. The simulation results show that the proposed appliances scheduling approach can smooth the power consumption profile and minimise the electricity cost while considering user's preferences, user's feedbacks on each action taken and his/her preference settings. A user-interface is developed in MATLAB/Simulink for the Home Energy Management System (HEMS) to demonstrate the proposed DR scheme. The simulation tool includes features such as smart appliances, electricity pricing signals, smart meters, solar photovoltaic generation, battery energy storage, electric vehicle and grid supply.

With the development of Internet of Things(IoT), more and more smart devices and services appeared in people's life. These services make life more convenient, while it is difficult for people, especially the elderly, to find them in time, because they are widely distributed in great numbers with small sizes. In order to solve this problem, the service recommendation scheme, which is becoming increasingly important, needs to be used in IoT. However, the traditional web service recommendation schemes are not suitable for the IoT, because they rely more on the historical information rather than the energy state or the user's habit attributes, which are important for IoT. We research the service recommendation scheme, finding that the service recommendation process in IoT is a process of matter flows, which follows the conservation of matter. Therefore, we propose the service recommendation scheme based on tripartite graph with matter diffusion and use the habit feature as a dynamic tag. Based on the balance of matter, we use the positive and negative matter diffusion results on the tripartite as the recommendation results. The results of our evaluation show that the performance of the service recommendation scheme is improved in the precision and recall.

Smart home is an emerging form of the Internet of things (IoT), which provides a convenient and comfortable living environment for the smart home users. With the explosive growth of smart home information, people pay more attention to the privacy protection of smart home, including the choice of privacy and effective privacy protection scheme. This paper proposes a privacy protection scheme based on information hiding, the scheme guarantees the sensitive data transmitted securely. First, the smart home Real-time sensor data are classified into sensitive data and non-sensitive data through machine learning, the process can be controlled according to the user's preferences. Second, the sensitive data can be transmitted securely by ordinary channels using method of combination encryption with information hiding. Experiment results show that this scheme can help users to express their preferences to the privacy protection, changing the previous practice that users can only accept the privacy settings provided by the supplier. What's more, the scheme combines encryption with information hiding, providing a double guarantee for the sensitive data during they are transmitted on ordinary channels. Comparing to the previous practice of using encryption merely, this scheme greatly improves the security of data transmission and is more suitable for the real environment.

With the increasing aging today, the rich life experience has become a topic that people are urgently concerned about. This research mainly discusses the research and application of VR-based smart home image analysis system. This research combines the intelligent design and implementation of the image analysis system of VR technology. The system uses 2D image processing, segmentation, and recognition to filter out information that is of interest to the image, which is helpful for a reasonable plan for smart homes. During the experiment, a mixed model was used to model the background scene, the background difference method was used to detect the moving target in the image, and then the face detection of the moving target was performed. Through such simple real-time image processing, the necessity of real-time video monitoring is realized. After that, the abnormal image data can be saved to provide effective evidence, and the system will issue a warning after detecting the abnormality, which can effectively monitor specific areas such as home. 110 frames of video clips of people actually walking indoors are used for face detection. Experimentally, the acquisition speed of the VR technology camera is 24 frames per second, and the resolution is 420*250. The research results show that image processing is an indispensable part of the intelligent process of video surveillance. It can also be said that the image processing technology of VR technology is more suitable for the smart and suitable home.

The impressive advantages offered by of demand-side participation have accelerated deployment of demand response (DR) programs. However, the first step to attain the benefits of DR programs is to increase awareness level of the customers. This paper proposes a simple-but-efficient platform to enlighten the costumers on manifested merits of demand-side load control. The proposed platform is a web-based application which acquires the load profile of the customer, associated flexible appliances, and the customer preferences for using the appliances. In turn, presents the optimal operation schedule for flexible appliances and attained benefits from using the optimal schedule. To calculate the optimal operation schedule, a mixed-integer linear optimization model is devised where the decision variables are settings of flexible appliances, charge/discharge status and amount of storage device, charge/discharge status, and amount of electrical vehicle. The devised optimization engine is linked to a database to acquire required data for optimization which encompasses historical data for customer load, forecasts of renewables, ratings of customers’ flexible appliances, and subjected energy tariff. The attained optimal scheduling for the customer is then returned to the database. On the other hand, the database is linked to the web-based user interface to get the user preferences (write to the database) and represent the recommendation for optimal operation and attained benefits (read from database). To manage the links between web-based user interface, database, and optimization tool, proper linking application programming interfaces (APIs) are devised. The proposed platform is testified using real-world data and its effectiveness is assured by experimental studies.

Location-based services (LBSs) for elderly care is a trending topic in smart homes. The key issue is the high accurate positioning for the elderly. Ultra-wideband (UWB) is a centimeter-level positioning accuracy in line-of-sight (LOS) environments. However, most of existing UWB positioning methods need non-line-of-sight (NLOS) identification and compensation, and thus leading to severe deterioration in positioning accuracy in presence of complex indoor environments where the elderly lived. This article proposes a phase-difference-of-arrival (PDOA) assisted UWB positioning method (PDOA-UWB) for elderly care. In our positioning framework, we first calculate the phase difference of arrival from PDOA chip integrated in the UWB base stations (BSs) to obtain a coarse location of the elderly, which is further used to distinguish which nearby BSs are in LOS environments and which are in NLOS environments. Then, a PDOA-UWB positioning solution is derived to improve the positioning accuracy of the elderly. Compared with some existing methods, our method achieves higher accuracy with less NLOS compensation, and is easier to be implemented in complex indoor environments for simple practical engineering applications. Experimental results show the efficacy of our proposed method.

Smart home environments account to a major portion of the total energy consumption in today's world. The residents of smart home environments wish to find solutions that reduce the energy costs along with providing an optimal indoor environment for the residents. Another significant aspect in smart home systems is efficiency of tasks management and control commands' execution for smart home actuators. In this paper, we propose an optimal control solution for smart home environment based on smart home energy optimization and control tasks' load dispatching and scheduling. Optimal control is achieved by first defining an objective function for minimizing energy cost which is implemented using VB-PSO (velocity boost particle swarm optimization) algorithm. Next, the control tasks are generated using rule set implemented in fuzzy logic; defined based on optimal values achieved from VB-PSO. A Markov model based mechanism dispatches control tasks at scheduler, for efficient scheduling and optimal control. The results show that the proposed optimization scheme saves up to 29.73% energy costs on average, in comparison to baseline scheme. The proposed tasks' load dispatching scheme of admission control, makes the job of load balancing among the processors efficient while giving priority to the urgent tasks. The results for scheduler evidently show the low dropping probabilities for urgent tasks along with showing 34.9% reduction in tasks' starvation rate and 36.82% reduction in average tasks' instances missing rates.

The Internet of things (IoT) became quickly one of the most popular and most discussed topics in research. Studies paid attention to the Internet stuff, primarily to new products that aim to achieve greater efficiency and simplicity in life. IoT may cover several fields of the smart environment. Because of the data exposure that occurs when data is transferred via various channels, data protection issues have become a major problem as the company continues to expand. When user privacy and property are taken into consideration, the situation may become much worse. As a result, the authentication process for communicating entities has garnered considerable attention. In this paper, we proposed a secure authentication model for smart home applications, which privacy considered and com-plies with the General Data Protection Regulation GDPR. The proposed scheme improved the existing authentication schemes’ performance and security level. This work based on the Elliptic curve cryptography ECC, one-way hash function, and XOR operation. The proposed lightweight authentication model is suitable for resource-constrained devices. This study is developing the offline direct authentication model to authenticate users and IoT devices in the local net-work. In addition, our scheme uses the online authentication server to authenticate all system parts. © 2022, Tech Science Press. All rights reserved.

In a storytelling context, the visualization of the timelines for appliance usage could convey a great amount of information to audiences about the patterns or habits of households when using appliances in smart home. However, today's research on smart home visualization is more focused on time series data, while research on timeline does not focus much on expressiveness in a storytelling context. If we simply visualize the time series or use traditional visualization methods to visualize the timeline, the intention and effectiveness of storytelling may be reduced. In this paper, we analyze the above issues in detail and propose a new visualization design based on machinima and a dynamic circle.

Human activity recognition (HAR) is recently used in numerous applications including smart homes to monitor human behavior, automate homes according to human activities, entertainment, falling detection, violence detection, and people care. Vision-based recognition is the most powerful method widely used in HAR systems implementation due to its characteristics in recognizing complex human activities. This paper addresses the design of a 3D convolutional neural network (3D-CNN) model that can be used in smart homes to identify several numbers of activities. The model is trained using KTH dataset that contains activities like (walking, running, jogging, handwaving handclapping, boxing). Despite the challenges of this method due to the effectiveness of the lamination, background variation, and human body variety, the proposed model reached an accuracy of 93.33%. The model was implemented, trained and tested using moderate computation machine and the results show that the proposal was successfully capable to recognize human activities with reasonable computations.

End users have become active participants in local electricity market transactions because of the growth of the smart grid concept and energy storage systems (ESS). This participation is optimized in this article using a stochastic two-stage model considering the day-ahead and real-time electricity market data. This model optimally schedules the operation of a Smart Home (SH) to meet its energy demand. In addition, the uncertainty of wind and photovoltaic (PV) generation is considered along with different appliances. In this paper, the participation of an EV (electric vehicle), together with the battery energy storage systems, which allow for the increase in bidirectional energy transactions are considered. Demand Response (DR) programs are also incorporated which consider market prices in real-time and impact the scheduling process. A comparative analysis of the performance of a smart home participating in the electricity market is carried out to determine an optimal DR schedule for the smart homeowner. The results show that the SH’s participation in the real-time pricing, scheme not only reduces the operating costs but also leads to better than expected profits. Moreover, total, day-ahead, and real-time expected profits are better in comparison with existing literature. The objective of this paper is to analyze the SH performance within the electrical market context so as to increase the system’s flexibility whilst optimizing DR schedules that can mitigate the variability of end-users generation and load demand.

With the expansion of power electronics possibilities for smart homes, new perceptions for power control are emerging, suggesting new possibilities also for smart grids. In this prospect, the solid-state transformer (SST) has a substantial impact to interface smart homes with smart grids, guaranteeing high levels of power quality in both grid (consumed current) and load side (produced voltage). Nevertheless, as advanced contributions, the SST can deal with other possibilities of controllability. In such situation, an analysis of new operation opportunities for the SST into smart homes and smart grid perspectives is offered in this paper. It is discussed the SST principle of operation, with a thorough clarification concerning the proposed control algorithms, as well as an intuitive computational validation contemplating contingencies of operation about power quality effects for the load and grid side. The attained results strengthen the attractiveness of the new operation opportunities for the SST when utilized as interface between homes and smart grids., This work has been supported by FCT -Fundacao para a Ciencia e Tecnologia within the R&D Units Project Scope: UIDB/00319/2020. This work has been supported by the FCT Project newERA4GRIDs PTDC/EEIEEE/30283/2017.

The home of the future will surely be a technologically richer environment, offering tens or even hundreds of network services ranging from the simple continuous transmission of video data streams to a complex system of house energy management. The typical elements within the house that can be controlled are: lighting, windows and shutters, doors, speakers, heating, ventilation, air conditioning, appliances (refrigerator, washing machine, ovens, etc.), audio-video systems, burglar alarms and fire alarms, telecommunications.Many operational control sequences can be automated to prevent the user from making the effort to adapt to systems. Some of these services will be provided by devices inside the home and others by external providers. So a great improvement to this control can happen by adding a certain degree of "intelligence" to the system allowing it to recognize recurring activities, dangerous or unusual situations in order to anticipate health problems or special needs of users within of the House. These problems can be addressed by monitoring the activities of regular users, which allows the creation of rules-based profiles, in order to grasp and formalize their normal behavior.In order to realize Smart Home systems through IoT, the problem of localization within closed spaces remains constant: in fact, efficiency is put to the test by all those that are the vertical closures (opinions and fixtures) with which optimal transmission with the outside is possible. The Beacon technology was used to solve the indoor transmission.The Beacon is a device that through Bluetooth Low Energy technology, is able to transmit information to mobile devices with a certain range of action.In this paper, for the Beacons, different methods of localization in a closed environment are tested and demonstrated through a mapping of the signal in a room, has led to satisfactory results. The relationship between the used beacon and area covered by their own signal is also identified in a beacon every 5 square meters; this result allows us to have an idea of the costs of mapping a given environment and the invasion of the technology inside it. A further raised question of the relationship between used beacon and covered area was how and where to place the beacons inside the room; The less satisfactory result concerns the error, albeit low, detected in the pure localization (ie without the help of external information such as the initial position) in a closed environment.

This article presents a quad-band multiple-input-multiple-output (MIMO) antenna for the Internet of Things (IoT) applications. The proposed antenna consists of four quarter-wavelength asymmetrical meandered radiators, microstrip feed lines, and modified ground planes. The antenna elements are arranged in a chiral pattern to improve isolation between them, with two radiators and two ground planes placed on the front side of the substrate and the other two on the back side. The MIMO antenna has an operating bandwidth (S11 ≤ −10 dB) of 1.76–1.84 GHz, 2.37–2.56 GHz, 3.23–3.68 GHz, and 5.34–5.84 GHz, covering GSM, WLAN, WiMAX, and 5G frequency bands. The isolation between the radiating elements is greater than 18 dB in the operating bands. The peak gain of the antenna is 3.6 dBi, and the envelope correlation coefficient (ECC) is less than 0.04. Furthermore, the proposed antenna is validated for IoT-based smart home (SH) applications. The prototype MIMO antenna is integrated with a commercially available ZigBee device, and the measured values are found to be consistent with the expected results. The proposed MIMO antenna could be a good candidate for IoT systems/modules due to its low profile, compact size, lightweight, and easy integration with wireless communication devices.

The development of information and communication technology in terms of sensor technologies cause the Internet of Things (IoT) step toward smart homes for prevalent sensing and management of resources. The gateway connections contain various IoT devices in smart homes representing the security based on the centralized structure. To address the security purposes in this system, the blockchain framework is considered a smart home gateway to overcome the possible attacks and apply Deep Reinforcement Learning (DRL). The proposed blockchain-based smart home approach carefully evaluated the reliability and security in terms of accessibility, privacy, and integrity. To overcome traditional centralized architecture, blockchain is employed in the data store and exchange blocks. The data integrity inside and outside of the smart home cause the ability of network members to authenticate. The presented network implemented in the Ethereum blockchain, and the measurements are in terms of security, response time, and accuracy. The experimental results show that the proposed solution contains a better outperform than recent existing works. DRL is a learning-based algorithm which has the most effective aspects of the proposed approach to improve the performance of system based on the right values and combining with blockchain in terms of security of smart home based on the smart devices to overcome sharing and hacking the privacy. We have compared our proposed system with the other state-of-the-art and test this system in two types of datasets as NSL-KDD and KDD-CUP-99. DRL with an accuracy of 96.9% performs higher and has a stronger output compared with Artificial Neural Networks with an accuracy of 80.05% in the second stage, which contains 16% differences in terms of improving the accuracy of smart homes.