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The Internet of Things (IoT) presents immense opportunities for driving Industry 4.0 forward. However, in scenarios involving networked control automation, ensuring high reliability and predictable latency is vital for timely responses. To meet these demands, the contemporary wireless protocol time-slotted channel hopping (TSCH), also referred to as IEEE 802.15.4-2015, relies on precise transmission schedules to prevent collisions and achieve consistent end-to-end traffic flow. In the realm of diverse IoT applications, this study introduces a new traffic-aware autonomous multi-objective scheduling function called OPTIMAOrchestra. This function integrates slotframe and channel management, adapts to varying network sizes, supports mobility, and reduces collision risks. The effectiveness of two versions of OPTIMAOrchestra is extensively evaluated through multi-run experiments, each spanning up to 3600 s. It involves networks ranging from small-scale setups to large-scale deployments with 111 nodes. Homogeneous and heterogeneous network topologies are considered in static and mobile environments, where the nodes within a network send packets to the server with the same and different application packet intervals. The results demonstrate that OPTIMAOrchestra_ch4 achieves a current consumption of 0.72 mA while maintaining 100% reliability and 0.86 mA with a 100% packet delivery ratio in static networks. Both proposed Orchestra variants in mobile networks achieve 100% reliability, with current consumption recorded at 6.36 mA. Minimum latencies of 0.073 and 0.02 s are observed in static and mobile environments, respectively. On average, a collision rate of 5% is recorded for TSCH and RPL communication, with a minimum of 0% collision rate observed in the TSCH broadcast in mobile networks. Overall, the proposed OPTIMAOrchestra scheduler outperforms existing schedulers regarding network efficiency, time, and usability, significantly improving reliability while maintaining a balanced latency–energy trade-off.

Over the years, a rapid evolution of smart grids has been witnessed across the world due to their intelligent operations and control, smart characteristics, and benefits, which can overcome several difficulties of traditional electric grids. However, due to multifaceted technological advancements, the development of smart grids is evolving day by day. Thus, smart grid researchers need to understand and adapt to new concepts and research trends. Understanding these new trends in smart grids is essential for several reasons, as the energy sector undergoes a major transformation towards becoming energy efficient and resilient. Moreover, it is imperative to realize the complete potential of modernizing the energy infrastructure. In this regard, this paper presents a comprehensive bibliometric analysis of smart grid concepts and research trends. In the initial search, the bibliometric data extracted from the Scopus and Web of Science databases totaled 11,600 and 2846 records, respectively. After thorough scrutiny, 2529 unique records were considered for the bibliometric analysis. Bibliometric analysis is a systematic method used to analyze and evaluate the scholarly literature on a particular topic and provides valuable insights to researchers. The proposed analysis provides key information on emerging research areas, high-impact sources, authors and their collaboration, affiliations, annual production of various countries and their collaboration in smart grids, and topic-wise title count. The information extracted from this bibliometric analysis will help researchers and other stakeholders to thoroughly understand the above-mentioned aspects related to smart grids. This analysis was carried out on smart grid literature by using the bibliometric package in R.

With the rapid rise of digitalization in the global economy, home security systems have become increasingly important for personal comfort and property protection. The collaboration between humans, the Internet of Things (IoT), and smart homes can be highly efficient. Interaction considers convenience, efficiency, security, responsiveness, and automation. This study aims to develop and assess IoT-based home security systems utilizing passive infrared (PIR) sensors to improve user interface, security, and automation controls using voice commands and buttons across different communication protocols. The proposed system incorporates controls for lighting and intrusion monitoring, as well as assessing both the functionality of voice commands and the precision of intruder detection via the PIR sensors. Intelligent light control and PIR intruder detection with a variable delay time for response detection are unified into the research methodology. The test outcomes examine the average effective response time in-depth, revealing performance distinctions among wireless fidelity (Wi-Fi) and fourth- and fifth-generation mobile connections. The outcomes illustrate the reliability of voice-activated light control via Google Assistant, with response accuracy rates of 83 percent for Thai voice commands and 91.50 percent for English voice commands. Moreover, the Blynk mobile application provided exceptional precision regarding operating light-button commands. The PIR motion detectors have a one hundred percent detection accuracy, and a 2.5 s delay is advised for PIR detection. Extended PIR detection delays result in prolonged system response times. This study examines the intricacies of response times across various environmental conditions, considering different degrees of mobile communication quality. This study ultimately advances the field by developing an IoT system prepared for efficient integration into everyday life, holding the potential to provide improved convenience, time-saving effectiveness, cost-efficiency, and enhanced home security protocols.

This paper evaluates the reliability of smart home energy management systems (SHEMSs) in a residential community with an unreliable power grid and power shortages. Unlike the previous works, which mainly focused on cost analysis, this research assesses the reliability of SHEMSs for different backup power sources, including photovoltaic systems (PVs), battery storage systems (BSSs), electric vehicles (EVs), and diesel generators (DGs). The impact of these changes on the daily cost and the balance of energy source contribution in providing electrical energy to household loads, particularly during power outage hours, is also evaluated. To address the uncertainty of electricity market prices, a risk management approach based on conditional value at risk is applied. Additionally, the study highlights the impact of community size on energy costs and reliability. The proposed model is formulated as a mixed-integer nonlinear programming problem and is solved using GAMS. The effectiveness of the proposed risk-based optimization approach is demonstrated through comprehensive cost and reliability analysis. The results reveal that when electric vehicles are used as backup power sources, the energy index of reliability (EIR) is not affected by market price variations and shows significant improvement, reaching approximately 99.9% across all scenarios.

In an aging society, the need for efficient emergency detection systems in smart homes is becoming increasingly important. For elderly people living alone, technical solutions for detecting emergencies are essential to receiving help quickly when needed. Numerous solutions already exist based on wearable or ambient sensors. However, existing methods for emergency detection typically assume that sensor data are error-free and contain no false positives, which cannot always be guaranteed in practice. Therefore, we present a novel method for detecting emergencies in private households that detects unusually long inactivity periods and can process erroneous or uncertain activity information. We introduce the Inactivity Score, which provides a probabilistic weighting of inactivity periods based on the reliability of sensor measurements. By analyzing historical Inactivity Scores, anomalies that potentially represent an emergency can be identified. The proposed method is compared with four related approaches on seven different datasets. Our method surpasses existing approaches when considering the number of false positives and the mean time to detect emergencies. It achieves an average detection time of approximately 05:23:28 h with only 0.09 false alarms per day under noise-free conditions. Moreover, unlike related approaches, the proposed method remains effective with noisy data.

Considering human influence and its negative impact on the environment, the world will have to transform the current energy system into a cleaner and more sustainable one. In residential as well as office buildings, there is a demand to minimize electricity consumption, improve the automation of electrical appliances and optimize electricity utilization. This paper describes the implementation of a smart switch with extended facilities compared to traditional switches, such as visual indication of evacuation routes in case of fire and acoustic alerts for emergencies. The proposed embedded system implements Modbus RTU serial communication to receive information from a fire alarm-control panel. An extension to the Modbus communication protocol, called Modbus Extended (ModbusE), is also proposed for smart switches and emergency switchboards. The embedded smart switch described in this paper as a scientific and practical contribution in this field, based on a performant microcontroller system, is integrated into the Building Internet of Things (BIoT) concept and uses the innovative ModbusE protocol. The proposed smart lighting system integrates building lighting access control for smart switches and sockets and can be extended to incorporate functionality for smart thermostats, access control and smart sensor-based information acquisition.

Smart security devices, such as smart locks, smart cameras, and smart intruder alarms are increasingly popular with users due to the enhanced convenience and new features that they offer. A significant part of this convenience is provided by the device’s companion smartphone app. Information on whether secure and ethical development practices have been used in the creation of these applications is unavailable to the end user. As this work shows, this means that users are impacted both by potential third-party attackers that aim to compromise their device, and more subtle threats introduced by developers, who may track their use of their devices and illegally collect data that violate users’ privacy. Our results suggest that users of every application tested are susceptible to at least one potential commonly found vulnerability regardless of whether their device is offered by a known brand name or a lesser-known manufacturer. We present an overview of the most common vulnerabilities found in the scanned code and discuss the shortcomings of state-of-the-art automated scanners when looking at less structured programming languages such as C and C++. Finally, we also discuss potential methods for mitigation, and provide recommendations for developers to follow with respect to secure coding practices.

The number of connected devices or Internet of Things (IoT) devices has rapidly increased. According to the latest available statistics, in 2023, there were approximately 17.2 billion connected IoT devices; this is expected to reach 25.4 billion IoT devices by 2030 and grow year over year for the foreseeable future. IoT devices share, collect, and exchange data via the internet, wireless networks, or other networks with one another. IoT interconnection technology improves and facilitates people’s lives but, at the same time, poses a real threat to their security. Denial-of-Service (DoS) and Distributed Denial-of-Service (DDoS) attacks are considered the most common and threatening attacks that strike IoT devices’ security. These are considered to be an increasing trend, and it will be a major challenge to reduce risk, especially in the future. In this context, this paper presents an improved framework (SDN-ML-IoT) that works as an Intrusion and Prevention Detection System (IDPS) that could help to detect DDoS attacks with more efficiency and mitigate them in real time. This SDN-ML-IoT uses a Machine Learning (ML) method in a Software-Defined Networking (SDN) environment in order to protect smart home IoT devices from DDoS attacks. We employed an ML method based on Random Forest (RF), Logistic Regression (LR), k-Nearest Neighbors (kNN), and Naive Bayes (NB) with a One-versus-Rest (OvR) strategy and then compared our work to other related works. Based on the performance metrics, such as confusion matrix, training time, prediction time, accuracy, and Area Under the Receiver Operating Characteristic curve (AUC-ROC), it was established that SDN-ML-IoT, when applied to RF, outperforms other ML algorithms, as well as similar approaches related to our work. It had an impressive accuracy of 99.99%, and it could mitigate DDoS attacks in less than 3 s. We conducted a comparative analysis of various models and algorithms used in the related works. The results indicated that our proposed approach outperforms others, showcasing its effectiveness in both detecting and mitigating DDoS attacks within SDNs. Based on these promising results, we have opted to deploy SDN-ML-IoT within the SDN. This implementation ensures the safeguarding of IoT devices in smart homes against DDoS attacks within the network traffic.

In this paper, we present a novel non-deep-learning-based approach for real-time object tracking and activity recognition within smart homes, aiming to minimize human intervention and dataset requirements. Our method utilizes discreet, easily concealable sensors and passive RFID technology to track objects in real-time, enabling precise activity recognition without the need for extensive datasets typically associated with deep learning techniques. Central to our approach is AtomGID, an algorithm tailored to extract highly generalizable spatial features from RFID data. Notably, AtomGID’s adaptability extends beyond RFID to other imprecise tracking technologies like Bluetooth beacons and radars. We validate AtomGID through simulation and real-world RFID data collection within a functioning smart home environment. To enhance recognition accuracy, we employ a clustering adaptation of the flocking algorithm, leveraging previously published Activities of Daily Living (ADLs) data. Our classifier achieves a robust classification rate ranging from 85% to 93%, underscoring the efficacy of our approach in accurately identifying activities. By prioritizing non-deep-learning techniques and harnessing the strengths of passive RFID technology, our method offers a pragmatic and scalable solution for activity recognition in smart homes, significantly reducing dataset dependencies and human intervention requirements.

End-User Development has been proposed over the years to allow end users to control and manage their Internet of Things-based environments, such as smart homes. With End-User Development, end users are able to create trigger-action rules or routines to tailor the behavior of their smart homes. However, the scientific research proposed to date does not encompass methods that evaluate the suitability of user-created routines in terms of energy consumption. This paper proposes using Machine Learning to build a Digital Twin of a smart home that can predict the energy consumption of smart appliances. The Digital Twin will allow end users to simulate possible scenarios related to the creation of routines. Simulations will be used to assess the effects of the activation of appliances involved in the routines under creation and possibly modify them to save energy consumption according to the Digital Twin’s suggestions.

Within the context of a smart home, detecting the operating status of appliances in the environment plays a pivotal role, estimating power consumption, issuing overuse reminders, and identifying faults. The traditional contact-based approaches require equipment updates such as incorporating smart sockets or high-precision electric meters. Non-constant approaches involve the use of technologies like laser and Ultra-Wideband (UWB) radar. The former can only monitor one appliance at a time, and the latter is unable to detect appliances with extremely tiny vibrations and tends to be susceptible to interference from human activities. To address these challenges, we introduce HomeOSD, an advanced appliance status-detection system that uses mmWave radar. This innovative solution simultaneously tracks multiple appliances without human activity interference by measuring their extremely tiny vibrations. To reduce interference from other moving objects, like people, we introduce a Vibration-Intensity Metric based on periodic signal characteristics. We present the Adaptive Weighted Minimum Distance Classifier (AWMDC) to counteract appliance vibration fluctuations. Finally, we develop a system using a common mmWave radar and carry out real-world experiments to evaluate HomeOSD’s performance. The detection accuracy is 95.58%, and the promising results demonstrate the feasibility and reliability of our proposed system.

The 21st century has marked the dawn of an aging population. China’s aging process ranks first worldwide. The country has recognized the gravity of this demographic shift and implemented strategies to address it at the national level. A vast majority of elderly Chinese individuals (approximately 90%) aspire to age in their own homes. Smart homes, endowed with cutting-edge digital technologies, such as AI, the Internet of Things, and big data, hold vast potential for enabling this vision. However, acceptance of smart home products and services among elderly individuals in China remains low. The main reason is that the related products and services fail to effectively alleviate the perceived risk of this population in the R&D process of related products and services, and there is a lack of effective measurement methods. To holistically assess the potential obstacles faced by elderly individuals using smart home products and services, this study targeted individuals aged 45–60 years in China. This study aimed to develop a comprehensive perceived risk scale specific to smart homes for this demographic. Initially, this study identified key risk dimensions and corresponding measurement items through a rigorous literature review, user interviews, and expert consultations. Subsequently, it ensured the reliability and validity of each dimension and its corresponding observation variables through preliminary research, exploratory factor analysis, and confirmatory factor analysis. This approach allowed for a comprehensive understanding of the challenges faced by future elderly individuals when utilizing smart home products and services, thus enabling the development of more effective solutions. The scale encompassed ten factors and seventy measurement items, including Privacy and Security Risk (seven items), Physical Risk (seven items), Technological Risk (nine items), Performance Risk (seven items), Service Risk (nine items), Financial Risk (five items), Psychological Risk (seven items), Industry and Market Risk (six items), Social Support Risk (six items), and Policy and Legal risk (seven items). The measurement scale developed in this study represents a groundbreaking first attempt to create a systematic scale for assessing the perceived risks associated with smart homes for the elderly in China. It not only enables professionals, businesses, and manufacturers to avoid or reduce barriers in the R&D process of related products and services, facilitating smart home industry growth and enhancing user adoption, but also serves as a universal reference for the potential obstacles that digital technology may encounter in addressing aging-related issues, which has significant theoretical value and practical importance.

Modern homes are experiencing unprecedented levels of convenience because of the proliferation of smart devices. In order to improve communication between smart home devices, this paper presents a novel approach that particularly addresses interference caused by different transmission systems. The core of the suggested framework is an intelligent Internet of Things (IoT) system designed to reduce interference. By using adaptive communication protocols and sophisticated interference management algorithms, the framework minimizes interference caused by overlapping transmissions and guarantees effective data sharing. This can be accomplished by creating an optimization model that takes into account the dynamic nature of the smart home environment and intelligently allocates resources. By maximizing the signal quality at the destination and optimizing the distribution of frequency channels and transmission power levels, the model seeks to minimize interference. A deep learning technique is used to augment the optimization model by adaptively learning and predicting interference patterns from real-time observations and historical data. The experimental results show how effective the suggested hybrid strategy is. While the deep learning model adjusts to shifting interference dynamics, the optimization model efficiently controls resource allocation, leading to better data reception performance at the destination. The system’s robustness is assessed in various kinds of situations to demonstrate its flexibility in responding to changing smart home settings. This work not only offers a thorough framework for interference reduction but also clarifies how deep learning and mathematical optimization can work together to improve the dependability of data reception in smart homes.

With the development of smart grids and new power systems, the combination of non-intrusive load identification technology and smart home technology can provide users with the operating conditions of home appliances and equipment, thus reducing home energy loss and improving users’ ability to demand a response. This paper proposes a non-intrusive load decomposition model with a parallel multiscale attention mechanism (PMAM). The model can extract both local and global feature information and fuse it through a parallel multiscale network. This improves the attention mechanism’s ability to capture feature information over long time periods. To validate the model’s decomposition ability, we combined the PMAM model with four benchmark models: the Long Short-Term Memory (LSTM) recurrent neural network model, the Time Pooling-based Load Disaggregation Model (TPNILM), the Extreme Learning Machine (ELM), and the Load Disaggregation Model without Parallel Multi-scalar Attention Mechanisms (UNPMAM). The model was trained on the publicly available UK-DALE dataset and tested. The models’ test results were quantitatively evaluated using a confusion matrix. This involved calculating the F1 score of the load decomposition. A higher F1 score indicates better model decomposition performance. The results indicate that the PMAM model proposed in this paper maintains an F1 score above 0.9 for the decomposition of three types of electrical equipment under the same household user, which is 3% higher than that of the other benchmark models on average. In the cross-household test, the PMAM also demonstrated a better decomposition ability, with the F1 score maintained above 0.85, and the mean absolute error (MAE) decreased by 5.3% on average compared with that of the UNPMAM.

Detecting anomalies in human activities is increasingly crucial today, particularly in nuclear family settings, where there may not be constant monitoring of individuals’ health, especially the elderly, during critical periods. Early anomaly detection can prevent from attack scenarios and life-threatening situations. This task becomes notably more complex when multiple ambient sensors are deployed in homes with multiple residents, as opposed to single-resident environments. Additionally, the availability of datasets containing anomalies representing the full spectrum of abnormalities is limited. In our experimental study, we employed eight widely used machine learning and two deep learning classifiers to identify anomalies in human activities. We meticulously generated anomalies, considering all conceivable scenarios. Our findings reveal that the Gated Recurrent Unit (GRU) excels in accurately classifying normal and anomalous activities, while the naïve Bayes classifier demonstrates relatively poor performance among the ten classifiers considered. We conducted various experiments to assess the impact of different training–test splitting ratios, along with a five-fold cross-validation technique, on the performance. Notably, the GRU model consistently outperformed all other classifiers under both conditions. Furthermore, we offer insights into the computational costs associated with these classifiers, encompassing training and prediction phases. Extensive ablation experiments conducted in this study underscore that all these classifiers can effectively be deployed for anomaly detection in two-resident homes.

As living standards improve, the growing demand for energy, comfort, and health monitoring drives the increased importance of innovative solutions. Real-time recognition of human activities (HAR) in smart homes is of significant relevance, offering varied applications to improve the quality of life of fragile individuals. These applications include facilitating autonomy at home for vulnerable people, early detection of deviations or disruptions in lifestyle habits, and immediate alerting in the event of critical situations. The first objective of this work is to develop a real-time HAR algorithm in embedded equipment. The proposed approach incorporates the event dynamic windowing based on space-temporal correlation and the knowledge of activity trigger sensors to recognize activities in the case of a record of new events. The second objective is to approach the HAR task from the perspective of edge computing. In concrete terms, this involves implementing a HAR algorithm in a “home box”, a low-power, low-cost computer, while guaranteeing performance in terms of accuracy and processing time. To achieve this goal, a HAR algorithm was first developed to perform these recognition tasks in real-time. Then, the proposed algorithm is ported on three hardware architectures to be compared: (i) a NUCLEO-H753ZI microcontroller from ST-Microelectronics using two programming languages, C language and MicroPython; (ii) an ESP32 microcontroller, often used for smart-home devices; and (iii) a Raspberry-PI, optimizing it to maintain accuracy of classification of activities with a requirement of processing time, memory resources, and energy consumption. The experimental results show that the proposed algorithm can be effectively implemented on a constrained resource hardware architecture. This could allow the design of an embedded system for real-time human activity recognition.

The smart home is amongst the most promising areas of development of communication and information technologies. In order to assure safety, comfort, and resource conservation for all users, a smart home should be viewed as a high-tech system that combines the benefits of automation technologies and contemporary construction methods. Installation of the system is possible during the construction of new buildings as well as during the reconstruction of existing buildings. In this study, the smart home system’s overall idea is considered, the necessity of using resource-saving systems and technologies is supported, and the integration of such systems with the reconstruction of low-rise residential buildings is examined. The study generated a representation of the smart home system for a particular reconstruction project as well as an application for controlling the system using a mobile device.

The primary contributors to elevated line losses in low-voltage distribution networks are three-phase load imbalances and variations in load peak–valley differentials. The conventional manual phase sequence adjustment fails to capitalize on the temporal characteristics of the load, and the proliferation of smart homes has opened up new scheduling possibilities for managing the load. Consequently, this paper introduces a loss-reduction method for low-voltage distribution networks that leverages load-timing characteristics and adjustment capabilities. This method combines dynamic and static methods to reduce energy consumption from different time scales. To commence, this paper introduced a hierarchical fuzzy C-means algorithm (H-FCM), taking into account the distance and similarity of load curves. Subsequently, a phase sequence adjustment method, grounded in load-timing characteristics, was developed. The typical user load curve, derived from the classification of user loads, serves as the foundation for constructing a long-term commutation model, therefore mitigating the impact of load fluctuations on artificial commutation. Following this, this paper addressed the interruptible and transferable characteristics of various smart homes. This paper proposed a multi-objective transferable load (TL) optimal timing task adjustment model and a peak-shaving control strategy specifically designed for maximum sustainable power reduction of temperature-controlled loads (TCL). These strategies aim to achieve real-time load adjustment, correct static commutation errors, and reduce peak-to-valley differences. Finally, a simulation verification model was established in MATLAB (R2022a). The results show that the proposed method mainly solves the problems of three-phase imbalance and large load peak–valley difference in low-voltage distribution networks and reduces the line loss of low-voltage distribution networks through manual commutation and load adjustment.

Activities of daily living (ADLs) are fundamental routine tasks that the majority of physically and mentally healthy people can independently execute. In this paper, we present a semantic framework for detecting problems in ADLs execution, monitored through smart home sensors. In the context of this work, we conducted a pilot study, gathering raw data from various sensors and devices installed in a smart home environment. The proposed framework combines multiple Semantic Web technologies (i.e., ontology, RDF, triplestore) to handle and transform these raw data into meaningful representations, forming a knowledge graph. Subsequently, SPARQL queries are used to define and construct explicit rules to detect problematic behaviors in ADL execution, a procedure that leads to generating new implicit knowledge. Finally, all available results are visualized in a clinician dashboard. The proposed framework can monitor the deterioration of ADLs performance for people across the dementia spectrum by offering a comprehensive way for clinicians to describe problematic behaviors in the everyday life of an individual.

Deep learning technology can improve sensing efficiency and has the ability to discover potential patterns in data; the efficiency of user behavior recognition in the field of smart homes has been further improved, making the recognition process more intelligent and humanized. This paper analyzes the optical sensors commonly used in smart homes and their working principles through case studies and explores the technical framework of user behavior recognition based on optical sensors. At the same time, CiteSpace (Basic version 6.2.R6) software is used to visualize and analyze the related literature, elaborate the main research hotspots and evolutionary changes of optical sensor-based smart home user behavior recognition, and summarize the future research trends. Finally, fully utilizing the advantages of cloud computing technology, such as scalability and on-demand services, combining typical life situations and the requirements of smart home users, a smart home data collection and processing technology framework based on elderly fall monitoring scenarios is designed. Based on the comprehensive research results, the application and positive impact of optical sensors in smart home user behavior recognition were analyzed, and inspiration was provided for future smart home user experience research.

The rapid advancement in technology and rise in energy consumption have motivated research addressing Demand-Side Management (DSM). In this research, a novel design for Home Energy Management (HEM) is proposed that seamlessly integrates Battery Energy Storage Systems (BESSs), Photovoltaic (PV) installations, and Electric Vehicles (EVs). Leveraging a Mixed-Integer Linear Programming (MILP) approach, the proposed system aims to minimize electricity costs. The optimization model takes into account Real-Time Pricing (RTP) tariffs, facilitating the efficient scheduling of household appliances and optimizing patterns for BESS charging and discharging, as well as EV charging and discharging. Both individual and multiple Smart Home (SH) case studies showcase noteworthy reductions in electricity costs. In the case of multiple SHs, a remarkable cost reduction of 46.38% was achieved compared to a traditional SH scenario lacking integration of a PV, BESS, and EV.

In today’s power system landscape, renewable energy (RE) resources play a pivotal role, particularly within the residential sector. Despite the significance of these resources, the intermittent nature of RE resources, influenced by variable weather conditions, poses challenges to their reliability as energy resources. Addressing this challenge, the integration of an energy storage system (ESS) emerges as a viable solution, enabling the storage of surplus energy during peak-generation periods and subsequent release during shortages. One of the great challenges of ESSs is how to design ESSs efficiently. This paper focuses on a distributed power-flow system within a smart home environment, comprising uncontrollable power generators, uncontrollable loads, and multiple energy storage units. To address the challenge of minimizing energy loss in ESSs, this paper proposes a novel approach, called energy-efficient storage capacity with loss reduction (SCALE) scheme, that combines multiple-load power-flow assignment with a load-shifting algorithm to minimize energy loss and determine the optimal energy storage capacity. The optimization problem for optimal energy storage capacity is formalized using linear programming techniques. To validate the proposed scheme, real experimental data from a smart home environment during winter and summer seasons are employed. The results demonstrate the efficacy of the proposed algorithm in significantly reducing energy loss, particularly under winter conditions, and determining optimal energy storage capacity, with reductions of up to 11.4% in energy loss and up to 62.1% in optimal energy storage capacity.

In the landscape of the Fourth Industrial Revolution, the integration of the Internet of Things (IoT) in smart-home technology presents intricate challenges for digital forensics. This study investigates these challenges, focusing on developing forensic methodologies suitable for the diverse and complex world of smart-home IoT devices. This research is contextualized within the rising trend of interconnected smart homes and their associated cybersecurity vulnerabilities. Methodologically, we formulate a comprehensive approach combining open-source intelligence, application, network, and hardware analyses, aiming to accommodate the operational and data storage characteristics of various IoT devices. Extensive experiments were conducted on prevalent platforms, such as Samsung SmartThings, Aqara, QNAP NAS, and Hikvision IP cameras, to validate the proposed methodology. These experiments revealed crucial insights into the complexities of forensic data acquisition in smart-home environments, emphasizing the need for customized forensic strategies tailored to the specific attributes of various IoT devices. The study significantly advances the field of IoT digital forensics and provides a foundational framework for future explorations into broader IoT scenarios. It underscores the need for evolving forensic methodologies to keep pace with rapid technological advancements in IoT.

The ageing of populations is increasing pressure on health and social care systems. Potentially, assistive technologies are a way to support the independence of older adults in their daily activities. Among existing assistive technologies, ambient sensing technologies have received less attention than wearable systems. Moreover, there has been little research into cheaper technologies capable of using multiple modalities. A systematic review of the acceptability of assisted living or smart homes in the United Kingdom and the simultaneous use of sounds and vibrations in remote monitoring of assisted living or smart homes will inform and encourage the use of digital monitoring technologies. The acceptability of sensing technologies depends on whether there is any social stigma about their use, for example, the extent to which they invade privacy. The United Kingdom studies reviewed suggest a lack of measurements of the perceived efficacy or effectiveness of the monitoring devices. The primary use of vibration or acoustic technologies has been for detecting falls rather than monitoring health. The review findings suggest the need for further exploration of the acceptability and applicability of remote monitoring technologies, as well as a need for more research into the simultaneous use of sounds and vibrations in health monitoring.

This paper provides a comprehensive review of solutions based on artificial intelligence (AI) in the urban energy sector, with a focus on their applications and impacts. The study employed a literature review methodology to analyze recent research on AI’s role in energy-related solutions, covering the years 2019 to 2023. The authors classified publications according to their main focus, resulting in two key areas of AI implementation: residential and individual user applications, and urban infrastructure integration for society. The objectives of this review of the literature are the following: O1: to identify trends, emerging technologies, and applications using AI in the energy field; O2: to provide up-to-date insights into the use of AI in energy-related applications; O3: to gain a comprehensive understanding of the current state of AI-driven urban energy solutions; O4: to explore future directions, emerging trends, and challenges in the field of AI-driven energy solutions. This paper contributes to a deeper understanding of the transformative potential of AI in urban energy management, providing valuable insights and directions for researchers and practitioners in the field. Based on the results, it can be claimed that AI connected to energy at homes is used in the following areas: heating and cooling, lighting, windows and blinds, home devices, and energy management systems. AI is integrating into urban infrastructure through the following solutions: enhancement of electric vehicle charging infrastructure, reduction in vehicle emissions, development of smart grids, and efficient energy storage. What is more, the latest challenges associated with the implementation of AI-driven energy solutions include the need to balance resident comfort with energy efficiency in smart homes, ensuring compatibility and cooperation among various devices, preventing unintended energy consumption increases due to constant connectivity, the management of renewable energy sources, and the coordination of energy consumption.

Consumer Internet of Things (CIoT) manufacturers seek customer feedback to enhance their products and services, creating a smart ecosystem, like a smart home. Due to security and privacy concerns, blockchain-based federated learning (BCFL) ecosystems can let CIoT manufacturers update their machine learning (ML) models using end-user data. Federated learning (FL) uses privacy-preserving ML techniques to forecast customers’ needs and consumption habits, and blockchain replaces the centralized aggregator to safeguard the ecosystem. However, blockchain technology (BCT) struggles with scalability and quick ledger expansion. In BCFL, local model generation and secure aggregation are other issues. This research introduces a novel architecture, emphasizing gateway peer (GWP) in the blockchain network to address scalability, ledger optimization, and secure model transmission issues. In the architecture, we replace the centralized aggregator with the blockchain network, while GWP limits the number of local transactions to execute in BCN. Considering the security and privacy of FL processes, we incorporated differential privacy and advanced normalization techniques into ML processes. These approaches enhance the cybersecurity of end-users and promote the adoption of technological innovation standards by service providers. The proposed approach has undergone extensive testing using the well-respected Stanford (CARS) dataset. We experimentally demonstrate that the proposed architecture enhances network scalability and significantly optimizes the ledger. In addition, the normalization technique outperforms batch normalization when features are under DP protection.

The rapid growth of the energy and transport sectors has led to an increase in fuel consumption, resulting in a significant rise in greenhouse gas emissions. Switching to renewable energy sources and replacing internal combustion engines with electric vehicles (EVs) can significantly reduce greenhouse gas emissions. In recent years, the electrification of the transportation sector has become a primary focus of research and development efforts. However, if EVs are charged using conventional energy sources, we are unable to fully capitalize on their potential to reduce emissions. Charging EVs using renewable energy sources is the optimal solution. Otherwise, the increased number of EVs on the roads can significantly impact the stability of existing electric grids. As a result, smart homes with EV charging stations are becoming increasingly popular worldwide. This review focuses on the concept of grid-connected rooftop solar photovoltaic (PV) smart homes integrated with EVs and energy management systems in Australia. Australia can reduce emissions in the building and transport sectors by electrifying a range of vehicles and ultimately powering them with 100% renewable energy sources. The benefits of EV integration alongside rooftop solar systems for smart homes with house-to-vehicle or vehicle-to-house, as well as vehicle-to-grid or grid-to-vehicle (bidirectional EV charging) capabilities are also explored in this article. By adopting these systems, these smart homes can provide energy schemes for commercial use, ultimately contributing to the owner’s economic benefit.

Heating, ventilation, and air conditioning (HVAC) system performance research has received much attention in recent years. Many researchers suggest a set of appropriate fuzzy inputs that can be used to design fuzzy rules-based smart thermostats or controllers that can respond to demand-controlled ventilation, which in turn optimizes HVAC energy usage and provides satisfactory indoor temperatures. Previous research has focused on limited input parameters, such as indoor occupancy status, ambient temperature, and humidity constraints, which cannot efficiently and precisely manage thermal comfort. Hence, this study proposes a novel fuzzy controller with additional input parameters to keep indoor thermal comfort consistent with the corresponding number of occupants. The process employs an automatic fuzzy rule generation method to simplify the task of generating rules in the fuzzy inference system (FIS) using Mamdani FIS. A design-builder is used for modeling the HVAC systems. Local weather data were used to conduct simulations via EnergyPlus. The thermal comfort analysis using the Fanger model for three different scenarios shows that the proposed FIS controller can successfully respond to the indoor comfort variation in all possible scenarios and ensure a satisfactory comfort level. The proposed method demonstrates up to 50% energy savings if occupants do not worry about comfort.

Internet of Things (IoT) devices are common in today’s computer networks. These devices can be computationally powerful, yet prone to cybersecurity exploitation. To remedy these growing security weaknesses, this work proposes a new artificial intelligence method that makes these IoT networks safer through the use of autonomous, swarm-based cybersecurity penetration testing. In this work, the introduced Particle Swarm Optimization (PSO) penetration testing technique is compared against traditional linear and queue-based approaches to find vulnerabilities in smart homes and IoT networks. To evaluate the effectiveness of the PSO approach, a network simulator is used to simulate smart home networks of two scales: a small, home network and a large, commercial-sized network. These experiments demonstrate that the swarm-based algorithms detect vulnerabilities significantly faster than the linear algorithms. The presented findings support the case that autonomous and swarm-based penetration testing in a network could be used to render more secure IoT networks in the future. This approach can affect private households with smart home networks, settings within the Industrial Internet of Things (IIoT), and military environments.

Distributed smart home systems using wireless communication are increasingly installed and operated in households. Their popularity is due to the ease of installation and configuration. This paper presents a comprehensive technical, quality, and energy analysis of several popular smart home modules. Specifically, it focuses on verifying their power consumption levels, both in standby and active mode, to assess their impact on the energy efficiency of building installations. This is an important aspect in the context of their continuous operation, as well as in relation to the relatively lower power of loads popular in buildings, such as LED lighting. The author presents the results of measurements carried out for seven different smart home modules controlling seven different types of loads. The analysis of the results shows a significant share of home automation modules in the energy balance; in particular, the appearance of reactive power consumption due to the installation of smart home modules is noteworthy. Bearing in mind all the threads of the analysis and discussion of the results of measurement experiments, a short SWOT analysis is presented, with an indication of important issues in the context of further development of smart systems and the Internet of Things with wireless communication interfaces, dedicated to home and building applications.

Photovoltaic installations can be environmentally beneficial to a greater or lesser extent, depending on the conditions. If the energy produced is not used, it is redirected to the grid, otherwise a battery with a high ecological footprint is needed to store it. To alleviate this problem, an innovative recommender system is proposed for residents of smart homes equipped with battery-free solar panels to optimise the energy produced. Using artificial intelligence, the system is designed to predict the energy produced and consumed for the day ahead using three data sources: sensor logs from the home automation solution, data collected by the solar inverter, and weather data. Based on these predictions, recommendations are then generated and ranked by relevance. Data collected over 76 days were used to train two variants of the system, considering or without considering energy consumption. Recommendations selected by the system over 14 days were randomly picked to be evaluated for relevance, ranking, and diversity by 11 people. The results show that it is difficult to predict residents’ consumption based solely on sensor logs. On average, respondents reported that 74% of the recommendations were relevant, while the values contained in them (i.e., accuracy of times of day and kW energy) were accurate in 66% (variant 1) and 77% of cases (variant 2). Also, the ranking of the recommendations was considered logical in 91% and 88% of cases. Overall, residents of such solar-powered smart homes might be willing to use such a system to optimise the energy produced. However, further research should be conducted to improve the accuracy of the values contained in the recommendations.

One of the challenges in the field of human activity recognition in smart homes based on IoT sensors is the variability in the recorded data. This variability arises from differences in home configurations, sensor network setups, and the number and habits of inhabitants, resulting in a lack of data that accurately represent the application environment. Although simulators have been proposed in the literature to generate data, they fail to bridge the gap between training and field data or produce diverse datasets. In this article, we propose a solution to address this issue by leveraging the concept of digital twins to reduce the disparity between training and real-world data and generate more varied datasets. We introduce the Virtual Smart Home, a simulator specifically designed for modeling daily life activities in smart homes, which is adapted from the Virtual Home simulator. To assess its realism, we compare a set of activity data recorded in a real-life smart apartment with its replication in the VirtualSmartHome simulator. Additionally, we demonstrate that an activity recognition algorithm trained on the data generated by the VirtualSmartHome simulator can be successfully validated using real-life field data.

Smart home environments are a vital component of the larger ecosystem within smart cities, aiming to revolutionize residential living through the integration of Internet of Things (IoT) devices and advanced technologies. However, ensuring robust security and preserving privacy in these interconnected ecosystems present significant challenges. During the monitoring and controlling tasks in the smart home environment, diverse commands are exchanged between the IoT device and the user over the public Internet. The public Internet is open and vulnerable to various security attacks, which can corrode the monitoring and controlling operation of the smart home. In addition, conventional security algorithms are inappropriate for IoT devices deployed in the smart home. However, various pernicious security attacks are equally efficacious in the resource-limited smart home environment. Thus, various authenticated encryption schemes are proposed to enable security services in resource-constricted smart home environments. This paper presents a lightweight and efficient authentication framework for a smart home environment by leveraging the features of an authenticated encryption scheme and the hash function called “ESCI-AKA”. ESCI-AKA checks the authenticity of the user at the local device and exchanges three messages among the user, gateway, and smart embedded device for establishing a secure channel for indecipherable communication by setting a session key. In addition, we corroborate the security of the established session key through the random oracle model and informal security analysis. Moreover, the Scyther tool is employed for the security validation of ESCI-AKA. Finally, the performance comparison of ESCI-AKA and other eminent security frameworks explicates that ESCI-AKA requires low computational and communication costs while providing robust security features.

The rental of houses is a common economic activity. However, there are many inconveniences that arise when renting a property. The lack of trust between the landlord and the tenant due to fraud or squatters makes it necessary to involve third parties to minimize risk. A blockchain (such as Ethereum) provides an ideal solution to act as a low-cost intermediary. This paper proposes the use of non-fungible tokens (NFTs) based on ERC-4519 for smart home tokenization. The ERC-4519 is an Ethereum standard for describing NFTs tied to physical assets, allowing smart homes (assets) to be linked to NFTs so that the smart homes can interact with the blockchain and perform transactions, know their landlord (owner) and assigned tenant (user), whether they are authenticated or not, and know their operating mode (NFT state). The payments associated with the rental process are made using the NFT, eliminating the need for additional fungible tokens and simplifying the process. The entire rental process is described and illustrated with a proof of concept using a Pycom Wipy 3.0 as a smart home gateway and a smart contract programmed in Solidity, which is deployed on the Goerli Testnet for Ethereum. Experimental results show that the smart home gateway takes a few tens of milliseconds to complete a transaction, and the transaction costs of the relevant functions of the smart contract are quite affordable.

The paper contains a description of the building model structures and fuzzy control strategy. The aim of work was to describe fuzzy logic applied to the temperature control approach. The Simulink model of a widely popular building automation was used. The structure and the design aspects of a fuzzy logic control in the frame of classical control system manners of its realization were carried out. It can be viewed as a basement of intelligent control for further improvement. The simulation experiments with applied classical and fuzzy control action were conducted. The comparison results with conventional control law were obtained to highlight the advantages. The model of the room heating system and simulation results can be used and easy shifted to the real building management strategy.

Humanity’s way of life has been irreversibly transformed by new technological advancements during the past decades. Although such technological innovations have been gradually transposed into architecture, their full integration is not yet achieved. This article addresses the issue of incorporating cutting-edge technologies (such as smart thermostats, lighting sensors, security cameras, remote commands, graphic user interfaces, smartphones, mobile apps, gestures, voice commands, etc.) into urban small-scale residential architecture, in the future evolution context. For this purpose, a methodology was conceived that the main concepts regarding automation and information networks were researched, as well as their practice in some reference architecture cases. The guidelines for the prototype architectonic design were defined based on the previous knowledge acquired. Then, a prototype design of an intelligent home was iteratively developed as a machine for living in constant change. It was expected to contribute to increasing and disseminating knowledge in these fields, explaining their benefits and limitations. The prototype design presented in this article contributes to sensitizing architecture professionals to the importance of integrated and systematized thinking in all procedures of a smart home design.

Challenges caused by the care of the ageing population in many countries are becoming increasingly severe. However, high expenditures on healthcare services and the cost of infrastructure construction will be an unbearable burden for both the government and individuals. Due to its security, convenience, and lower prices compared with traditional medical care, smart home technology, which includes information and communication technologies (ICTs), artificial intelligence (AI), and the Internet of Things (IoT), can reduce the care burden. While smart home studies targeting older adults have increased, systematic analysis of these studies is lacking. CiteSpace enables rapid clustering and visualisation of the literature using scientific diagrams, exhibiting the results more comprehensibly. This analysis of academic research published within the last decade (from 2012 to 2023) through a scientometric approach can help to understand the historical variations, the latest technologies and emerging trends, and the research clusters that have emerged related to the study of smart homes for the elderly (SHFTE). The study results showed that recent research mainly centres on IoT for home automation and home-based healthcare, including wearable device applications and information security analysis. Research trends regarding SHFTE have focused primarily on developing and updating smart technologies based on the IoT, sensors, and related home monitoring. Therefore, greater emphasis must be placed on connecting smart homes and intelligent buildings with communities and societies, developing the potential of smart communities, and investigating the application of smart integration to the home and community in cities.

Technology anxiety contributes to an increased cognitive load and reduces user adoption of novel technologies. An illustrative example of this phenomenon is observed in the field of smart homes.To address this issue, we have identified an interaction paradigm known as natural interaction, which enables humans to engage with technology in a way that closely resembles natural human behavior and communication. This approach offers more user-friendly interactions that users are already familiar with, potentially reducing their cognitive load. In this paper, we present a case study of a smart lock system where we implement natural interaction and deploy it on a mixed reality (MR) device. By leveraging advanced features offered by MR head-mounted displays, we recreate the experience of people opening locks in everyday life. We conduct a user study comparing this interaction with traditional WIMP interaction in a mixed-reality environment. Through the analysis of collected data and user feedback, we examine the advantages and limitations of our proposed system.

Exploring future scenarios can consider future generations and society from a long-term perspective. A Futures Triangle is an approach used for mapping future scenarios. In general, the Futures Triangle collects weak signals using qualitative research methods. However, collecting weak signals qualitatively is limited by its small data size and manual data analysis errors. To overcome those limitations, this study proposes the data-driven futuristic scenario approach. This approach analyzes a large number of social perceptions existing in social networks as weak signals via semantic network analysis. Using our proposed data-driven approach, researchers can quantitatively collect weak signals for a Futures Triangle. To verify the applicability of the proposed method, we conducted a case study on the Chinese smart home service experience. The dataset consists of 2421 posts containing the keyword “smart home experience” on the Chinese social media platform Weibo. Three future scenarios were constructed using the proposed method. The results demonstrate the feasibility of the proposed methodology. The data-driven futuristic scenario approach has the advantage of quantitatively analyzing a large amount of stakeholder data to provide weak signals for the Futures Triangle. We suggest that the data-driven futuristic scenario approach serves as a supplementary method, combined with the traditional Futures Triangle approach, to comprehensively explore future scenarios.

The life expectancy of the elderly has substantially increased compared to earlier times. The primary factors are greater awareness of nutrition, the environment, and personal hygiene. Advances in science and technology have also extended the lifespans of the aging population. Traditional care methods are inadequate to address this situation. To maintain the socioeconomic structure, there is a need for the integration of advanced frameworks. In this context, we propose a smart framework called human activity Internet of Things-enabled environment (HAIE) to provide a non-human assistive environment that helps the elderly live independently. Research into aging in place and assistive environments has focused on modernized environments, largely neglecting the impact of technology on the lives of elderly individuals who stay at home. This work addresses the gap by integrating advanced technologies, such as cloud computing and the Internet of Things (IoT). The inclusion of IoT facilitates a smart and automated environment for the elderly. Cloud integration enables the storage of large volumes of data for further analysis and the identification of patterns for future advancements. It also introduces the concept of accessing data from anywhere, on any device, at any time. To validate this work, two primary parameters were considered: accuracy and latency. Through simulation, the proposed HAIE model has demonstrated an accuracy of 93% out of 100 attempts and a latency of 84.35 ms for the deployed case studies under the HAIE framework.

In traditional nonintrusive load monitoring (NILM) systems, the measurement device is installed upstream of an electrical system to acquire the total aggregate absorbed power and derive the powers absorbed by the individual electrical loads. Knowing the energy consumption related to each load makes the user aware and capable of identifying malfunctioning or less-efficient loads in order to reduce consumption through appropriate corrective actions. To meet the feedback needs of modern home, energy, and assisted environment management systems, the nonintrusive monitoring of the power status (ON or OFF) of a load is often required, regardless of the information associated with its consumption. This parameter is not easy to obtain from common NILM systems. This article proposes an inexpensive and easy-to-install monitoring system capable of providing information on the status of the various loads powered by an electrical system. The proposed technique involves the processing of the traces obtained by a measurement system based on Sweep Frequency Response Analysis (SFRA) through a Support Vector Machine (SVM) algorithm. The overall accuracy of the system in its final configuration is between 94% and 99%, depending on the amount of data used for training. Numerous tests have been conducted on many loads with different characteristics. The positive results obtained are illustrated and commented on.

Assistive devices can significantly improve caregivers’ ability to help disabled people with their daily activities. Existing assistive devices are not fully capable of safe transfer and are still in their early stages of development. In this research, a body-transfer system is designed and developed to ensure that the posture and body angle of the person in the sagittal plane remains unaltered while transferring from bed to wheelchair and vice versa. Two independently controlled conveyor belts (2-DOF) mounted on the indigenously developed bed are employed to transfer the disabled person using a sliding approach. Additionally, a wheelchair with conveyor belts that are fully automated is used to carry and transfer the user to and from the wheelchair. Furthermore, an integrated control architecture has been developed for safely operating the entire body-transfer system (from an indigenously developed bed and wheelchair). Finally, an experimental assessment of the body-transfer system’s performance has been conducted. The experimental findings demonstrate that the system can transfer up to 120 kg of body weight while the user’s posture remains unaltered in the sagittal plane. Users perceive a reduction in wrist and shoulder pain index using the body-transfer system. The system has great potential for relocating disabled persons safely while reducing the load on caregivers.

Although the smart home industry is rapidly emerging, it faces the risk of privacy security that cannot be neglected. As this industry now has a complex combination system involving multiple subjects, it is difficult for the traditional risk assessment method to meet these new security requirements. In this study, a privacy risk assessment method based on the combination of system theoretic process analysis–failure mode and effect analysis (STPA–FMEA) is proposed for a smart home system, considering the interaction and control of ‘user-environment-smart home product’. A total of 35 privacy risk scenarios of ‘component-threat-failure-model-incident’ combinations are identified. The risk priority numbers (RPN) was used to quantitatively assess the level of risk for each risk scenario and the role of user and environmental factors in influencing the risk. According to the results, the privacy management ability of users and the security state of the environment have significant effects on the quantified values of the privacy risks of smart home systems. The STPA–FMEA method can identify the privacy risk scenarios of a smart home system and the insecurity constraints in the hierarchical control structure of the system in a relatively comprehensive manner. Additionally, the proposed risk control measures based on the STPA–FMEA analysis can effectively reduce the privacy risk of the smart home system. The risk assessment method proposed in this study can be widely applied to the field of risk research of complex systems, and this study can contribute to the improvement of privacy security of smart home systems.

Daily activity recognition between different smart home environments faces some challenges, such as an insufficient amount of data and differences in data distribution. However, a deep network requires a large amount of labeled data for training. Additionally, inconsistent data distribution can lead to over-fitting of network learning. Additionally, the time cost of training the network from scratch is too high. In order to solve the above problems, this paper proposes a fine-tuning method suitable for daily activity recognition, which is the first application of this method in our field. Firstly, we unify the sensor space and activity space to reduce the variability in heterogeneous environments. Then, the Word2Vec algorithm is used to transform the activity samples into digital vectors recognizable by the network. Finally, the deep network is fine-tuned to transfer knowledge and complete the recognition task. Additionally, we try to train the network on public datasets. The results show that the network trained on a small dataset also has good transferability. It effectively improves the recognition accuracy and reduces the time cost and heavy data annotation.

The extensive application of the Internet of Things (IoT) and artificial intelligence technology has greatly promoted the construction and development of smart cities. Smart home as the foundation of smart cities can optimize home lifestyles. However, users access the smart home system through public channels, and the transmitted information is vulnerable to attack by attackers, and the privacy and data security of the home user will be difficult to be guaranteed. Therefore, how to protect users’ data and privacy security becomes critical. In this paper, we design a provably secure authentication scheme for the smart home environment, which ensures that only legitimate users can use smart devices. We use the informal model to verify the security of the scheme and formally analyze the security and correctness of the scheme through the Real or Random model. Finally, through the comparison of security and performance analysis, it is proven that our scheme has higher security under similar performance.

This work aims to create a web-based real-time monitoring system for electrical energy consumption inside a specific residence. This electrical energy is generated from a micro-CPV system lying on the roof of this residence. The micro-CPV is composed of a Fresnel lens as the main optical element, a spherical lens as the secondary optical element, and a multi-junction solar cell. A tiny photovoltaic concentrator system with a geometric concentration ratio of 100× is analyzed in the first part of this study, while the second part is designed to monitor the electricity generated by the micro-CPV system. An ESP8266 controller chipset is used to build the sensing peripheral node, which controls a relay and a PZEM-004T current sensor. As a result, the optical element used has approximately 83% optical efficiency, with an acceptance angle of 1.5°. Regarding the monitoring system, the architecture demonstrates the ability of the system to monitor current and energy consumption in real time using a computer or smartphone and a web server specially designed to continuously update the power consumption profile in a specific smart home environment. The whole electric power consumption monitoring system generally worked well. The monitoring system is configured to provide excellent accuracy for a 0.6% hit.

The expediential increase in Internet communication technologies leads to its expansion to interests beyond computer networks. MEMS (Micro Electro Mechanical Systems) can now be smaller with higher performance, leading to tiny sensors and actuators with enhanced capabilities. WSN (Wireless Sensor Networks) and IoT (Internet of Things) have become a way for devices to communicate, share their data, and control them remotely. Machine-to-Machine (M2M) scenarios can be easily implemented as the cost of the components needed in that network is now affordable. Some of these solutions seem to be more affordable but lack important features, while other ones provide them but at a higher cost. Furthermore, there are ones that can cover great distances and surpass the limits of a Smart Home, while others are more specialized for operation in small areas. As there is a variety of choices available, a more consolidated view of their characteristics is needed to figure out the pros and cons of each of these technologies. As there are a great number of technologies examined in this paper, they are presented regarding their connectivity: Wired, Wireless, and Dual mode (Wired and Wireless). Their oddities are examined with metrics based on user interaction, technical characteristics, data integrity, and cost factor. In the last part of this article, a comparison of these technologies is presented as an effort to assist home automation users, administrators, or installers in making the right choice among them.

Home appliances are considered to account for a large portion of smart homes’ energy consumption. This is due to the abundant use of IoT devices. Various home appliances, such as heaters, dishwashers, and vacuum cleaners, are used every day. It is thought that proper control of these home appliances can reduce significant amounts of energy use. For this purpose, optimization techniques focusing mainly on energy reduction are used. Current optimization techniques somewhat reduce energy use but overlook user convenience, which was the main goal of introducing home appliances. Therefore, there is a need for an optimization method that effectively addresses the trade-off between energy saving and user convenience. Current optimization techniques should include weather metrics other than temperature and humidity to effectively optimize the energy cost of controlling the desired indoor setting of a smart home for the user. This research work involves an optimization technique that addresses the trade-off between energy saving and user convenience, including the use of air pressure, dew point, and wind speed. To test the optimization, a hybrid approach utilizing GWO and PSO was modeled. This work involved enabling proactive energy optimization using appliance energy prediction. An LSTM model was designed to test the appliances’ energy predictions. Through predictions and optimized control, smart home appliances could be proactively and effectively controlled. First, we evaluated the RMSE score of the predictive model and found that the proposed model results in low RMSE values. Second, we conducted several simulations and found the proposed optimization results to provide energy cost savings used in appliance control to regulate the desired indoor setting of the smart home. Energy cost reduction goals using the optimization strategies were evaluated for seasonal and monthly patterns of data for result verification. Hence, the proposed work is considered a better candidate solution for proactively optimizing the energy of smart homes.

This paper defines a smart home use case to automatically adjust home temperature and/or hot water. The main objective is to reduce the energy consumption of cooling, heating and hot water systems in smart homes. To this end, the residents set a temperature (i.e., X degree Celsius) for home and/or hot water. When the residents leave homes (e.g., for work), they turn off the cooling or heating devices. A few minutes before arriving at their residences, the cooling or heating devices start working automatically to adjust the home or water temperature according to the residents’ preference (i.e., X degree Celsius). This can help reduce the energy consumption of these devices. To estimate the arrival time of the residents (i.e., drivers), this paper uses a machine learning-based street traffic prediction system. Unlike many related works that use machine learning for tracking and predicting residents’ behaviors inside their homes, this paper focuses on predicting resident behavior outside their home (i.e., arrival time as a context) to reduce the energy consumption of smart homes. One main objective of this paper is to find the most appropriate machine learning and neural network-based (MLNN) algorithm that can be integrated into the street traffic prediction system. To evaluate the performance of several MLNN algorithms, we utilize an Uber’s dataset for the city of San Francisco and complete the missing values by applying an imputation algorithm. The prediction system can also be used as a route recommender to offer the quickest route for drivers.

People on the autism spectrum have a different perception of the environment than neurotypical people and often require support in various activities of daily living. Assistive technology can support those affected, but very few smart-home-like technologies exist. To support people on the autism spectrum in their autonomy and safety and to help caregivers, a smart home and interior design environment was developed. Requirements were gathered by employing a holistic human-centered design approach through interactive workshops and questionnaires to create a useful and user-friendly solution. From this process, requirements for a comprehensive solution (the SENSHOME environment) emerged. These requirements include a set of functionalities tailored to the needs of people on the autism spectrum, such as a crowd warning that informs when many people are in a certain area (for example, the entrance), an automatic light regulation system, or a daily life planner that supports task completion. Furthermore, inclusive furniture elements such as a refuge seat or a table with dividers can support wellbeing, autonomy, and safety. This paper demonstrates a consequent and considerable participatory research approach and the story from the target group and context of use through design requirements to the initial design solution of the SENSHOME environment.