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Over the past century, in most countries, there has been a continual increase in life expectancy primarily due to improvements in public health, nutrition, personal hygiene and medicine. However, these improvements are now coupled with aging population demographics and falling birth rates, which, when combined, are expected to significantly burden the socioeconomic well-being of many of these countries. In fact, never before in human history have we been confronted with such a large aging population, nor have we developed solid, cost-effective solutions for the well-being, healthcare and social needs of the elderly. One efficient and cost-effective solution to the problem of elderly/patient care is remote healthcare monitoring so they can continue to live at home rather than in nursing homes or hospitals that are very expensive and with limited spaces. These remote monitoring systems will allow medical personnel to keep track of important physiological signs with reduced human resources, at less cost and in real time. This paper introduces several low-cost, noninvasive, user-friendly sensing and actuating systems using information and communication technologies. Such systems can be used to create engineering solutions to some of the pressing healthcare problems in our society, especially as it pertains to the elderly. One example is the integration of sensors, wireless communications, low-power electronics and intelligent computing to determine health-related information using signals from walking patterns. Such a sensing system will be suitable for prolonged use in a home environment. It will be wearable, noninvasive and non-intrusive, similar to smart socks , smart wrist - bands or smart belts . Other examples such as a smart joint monitor and a smart sleeping environment will be discussed, and future perspectives and research challenges in smart home technologies will be described.

The Internet of Things (IoT) is a broad concept that encompasses a variety of technologies and applications. As a result, having a comprehensive understanding of the future developments and deployment of this concept in various lateral and vertical markets and following future trend projections is critical. In this way, future studies might focus on numerous issues in areas where the Internet of Things is most widely used. This study focuses on the smart home setting, one of the most prevalent and fastest-growing applications of the Internet of Things idea. This field's current and future growth tendencies have been proven in many categories and capabilities of IoT devices such as comfort and lighting, energy, security, monitoring, media, and household appliances. The number of devices in use, houses with at least one IoT device, total revenue, and market value are increasing. With the positive growth trend of the smart home, the concept of cybersecurity is becoming one of the most critical issues whose overview we included in this research. Research results indicate several cybersecurity challenges raised due to the fast-growing trend of smart home concept that needs to be addressed in the future to prevent users' privacy and usability of various end devices in such environment.

One of the most challenging problems related to the operation of smart microgrids is the optimal home energy management scheme with multiple and conflicting objectives. Moreover, there is a noticeable increase in homes equipped with renewable energy sources (RESs), where the coordination of loads and generation can achieve extra savings and minimize peak loads. In this paper, a solar-powered smart home with optimal energy management is designed in an affordable and secure manner, allowing the owner to control the home from remote and local sites using their smartphones and PCs. The Raspberry Pi 4 B is used as the brain of the proposed smart home automation management system (HAMS). It is used to collect the data from the existing sensors and store them, and then take the decision. The home is monitored using a graphical interface that monitors room temperature, humidity, smoke, and lighting through a set of sensors, as well as PIR sensors to monitor the people movement. This action enables remote control of all home appliances in a safe and emission-free manner. This target is reached using Cayenne, which is an IoT platform, in addition to building some codes related to some appliances and sensors not supported in Cayenne from scratch. Convenience for people with disabilities is considered by using the Amazon Echo Dot (Alexa) to control home appliances and the charging point by voice, implementing the associated code for connecting the Raspberry pi with Alexa from scratch, and simulating the system on LabVIEW. To reach the optimal operation and reduce the operating costs, an optimization framework for the home energy management system (HEMS) is proposed. The operating costs for the day amounted to approximately 16.039 €. There is a decrease in the operating costs by about 23.13%. The consumption decreased after using the smart HAMS by 18.161 kWh. The results of the optimization also show that the least area that can be used to install solar panels to produce the desired energy with the lowest cost is about 118.1039 m2, which is about 23.62% of the total surface area of the home in which the study was conducted. The obtained results prove the effectiveness of the proposed system in terms of automation, security, safety, and low operating costs.

Health-monitoring smart homes are becoming popular, with experts arguing that 9-to-5 health care services might soon become a thing of the past. However, no review has explored the landscape of smart home technologies that aim to promote physical activity and independent living among a wide range of age groups. This review aims to map published studies on smart home technologies aimed at promoting physical activity among the general and aging populations to unveil the state of the art, its potential, and the research gaps and opportunities. Articles were retrieved from 6 databases (PubMed, CINAHL, Scopus, IEEE Xplore, ACM Library, and Web of Science). The criteria for inclusion were that the articles must be user studies that dealt with smart home or Active Assisted Living technologies and physical activity, were written in English, and were published in peer-reviewed journals. In total, 3 researchers independently and collaboratively assessed the eligibility of the retrieved articles and elicited the relevant data and findings using tables and charts. This review synthesized 20 articles that met the inclusion criteria, 70% (14/20) of which were conducted between 2018 and 2020. Three-quarters of the studies (15/20, 75%) were conducted in Western countries, with the United States accounting for 25% (5/20). Activities of daily living were the most studied (9/20, 45%), followed by physical activity (6/20, 30%), therapeutic exercise (4/20, 20%), and bodyweight exercise (1/20, 5%). K-nearest neighbor and naïve Bayes classifier were the most used machine learning algorithms for activity recognition, with at least 10% (2/20) of the studies using either algorithm. Ambient and wearable technologies were equally studied (8/20, 40% each), followed by robots (3/20, 15%). Activity recognition was the most common goal of the evaluated smart home technologies, with 55% (11/20) of the studies reporting it, followed by activity monitoring (7/20, 35%). Most studies (8/20, 40%) were conducted in a laboratory setting. Moreover, 25% (5/20) and 10% (2/20) were conducted in a home and hospital setting, respectively. Finally, 75% (15/20) had a positive outcome, 15% (3/20) had a mixed outcome, and 10% (2/20) had an indeterminate outcome. Our results suggest that smart home technologies, especially digital personal assistants, coaches, and robots, are effective in promoting physical activity among the young population. Although only few studies were identified among the older population, smart home technologies hold bright prospects in assisting and aiding older people to age in place and function independently, especially in Western countries, where there are shortages of long-term care workers. Hence, there is a need to do more work (eg, cross-cultural studies and randomized controlled trials) among the growing aging population on the effectiveness and acceptance of smart home technologies that aim to promote physical activity.

With the rapid development of information and communication technologies, smart homes are being investigated as effective solutions for home health care. The increasing academic attention on smart home health care has primarily been on the development and application of smart home technologies. However, comprehensive studies examining the general landscape of diverse research areas for smart home health care are still lacking. This study aims to determine the intellectual structure of smart home health care in a time series by conducting a coword analysis and topic analysis. Specifically, it investigates (1) the intellectual basis of smart home health care through overall academic status, (2) the intellectual foci through influential keywords and their evolutions, and (3) intellectual trends through primary topics and their evolutions. Analyses were conducted in 5 steps: (1) data retrieval from article databases (Web of Science, Scopus, and PubMed) and the initial dataset preparation of 6080 abstracts from the year 2000 to the first half of 2019; (2) data preprocessing and refinement extraction of 25,563 words; (3) a descriptive analysis of the overall academic status and period division (ie, 4 stages of 3-year blocks); (4) coword analysis based on word co-occurrence networks for the intellectual foci; and (5) topic analysis for the intellectual trends based on latent Dirichlet allocation (LDA) topic modeling, word-topic networks, and researcher workshops. First, regarding the intellectual basis of smart home health care, recent academic interest and predominant journals and research domains were verified. Second, to determine the intellectual foci, primary keywords were identified and classified according to the degree of their centrality values. Third, 5 themes pertaining to the topic evolution emerged: (1) the diversification of smart home health care research topics; (2) the shift from technology-oriented research to technological convergence research; (3) the expansion of application areas and system functionality of smart home health care; (4) the increased focus on system usability, such as service design and experiences; and (5) the recent adaptation of the latest technologies in health care. Based on these findings, the pattern of technology diffusion in smart home health care research was determined as the adaptation of technologies, the proliferation of application areas, and an extension into system design and service experiences. The research findings provide academic and practical value in 3 aspects. First, they promote a comprehensive understanding of the smart home health care domain by identifying its multifaceted intellectual structure in a time series. Second, they can help clinicians discern the development and dispersion level of their respective disciplines. Third, the pattern of technology diffusion in smart home health care could help scholars comprehend current and future research trends and identify research opportunities based on upcoming research waves of newly adapted technologies in smart home health care.

The power scheduling problem in a smart home (PSPSH) refers to the timely scheduling operations of smart home appliances under a set of restrictions and a dynamic pricing scheme(s) produced by a power supplier company (PSC). The primary objectives of PSPSH are: (I) minimizing the cost of the power consumed by home appliances, which refers to electricity bills, (II) balance the power consumed during a time horizon, particularly at peak periods, which is known as the peak-to-average ratio, and (III) maximizing the satisfaction level of users. Several approaches have been proposed to address PSPSH optimally, including optimization and non-optimization based approaches. However, the set of restrictions inhibit the approach used to obtain the optimal solutions. In this paper, a new formulation for smart home battery (SHB) is proposed for PSPSH that reduces the effect of restrictions in obtaining the optimal/near-optimal solutions. SHB can enhance the scheduling of smart home appliances by storing power at unsuitable periods and use the stored power at suitable periods for PSPSH objectives. PSPSH is formulated as a multi-objective optimization problem to achieve all objectives simultaneously. A robust swarm-based optimization algorithm inspired by the grey wolf lifestyle called grey wolf optimizer (GWO) is adapted to address PSPSH. GWO has powerful operations managed by its dynamic parameters that maintain exploration and exploitation behavior in search space. Seven scenarios of power consumption and dynamic pricing schemes are considered in the simulation results to evaluate the proposed multi-objective PSPSH using SHB (BMO-PSPSH) approach. The proposed BMO-PSPSH approach’s performance is compared with that of other 17 state-of-the-art algorithms using their recommended datasets and four algorithms using the proposed datasets. The proposed BMO-PSPSH approach exhibits and yields better performance than the other compared algorithms in almost all scenarios.

The smart home is a field that uses smart devices and gadgets to automate tasks and operations in the house, and it is expected that this study subject will become a focal point in future civilizations. The adoption of smart home services faces significant obstacles in terms of reliability and stability. The Software Defined Networking (SDN) paradigm offers up novel approaches and trends to embrace smart technologies to tackle the issues of smart homes. To avoid accidents, we developed a vertical SDN-based multi-level structure consisting of two controllers with a parent-child connection. It enables smart homes to make optimal use of current services while also providing new services to smart homes and buildings, opening the path for future smart services. In addition, we created an Application Programming Interface (API) for the smart home to make it easier to use current services and pave the path for future smart services. We designed numerous jobs depending on their categorization to give smart services, which is beneficial for smart homes. Local topologies, cloud topologies, and cloud-local topologies have all been compared in the evaluation section. The performance of POX, NOX, and Flood Light controllers is measured using three performance metrics: mean throughput, Round-Trip Time (RTT), and packet loss across all three topologies. Our findings revealed that the cloud-local topology had the lowest packet loss ratio of 1.4%, while the local and cloud topologies had 1.9% and 2.3%, respectively.

This study presents the design and development of an AI-based Smart Security System leveraging IoT technology for smart home applications. This research focuses on exploring and evaluating various artificial intelligence (AI) and Internet of Things (IoT) options, particularly in video processing and smart home security. The system is structured around key components: IoT technology elements, software management of IoT interactions, AI-driven video processing, and user information delivery methods. Each component’s selection is based on a comparative analysis of alternative approaches, emphasizing the advantages of the chosen solutions. This study provides an in-depth discussion of the theoretical framework and implementation strategies used to integrate these technologies into the security system. Results from the system’s deployment and testing are analyzed, highlighting the system’s performance and the challenges faced during integration. This study also addresses how these challenges were mitigated through specific adaptations. Finally, potential future enhancements are suggested to further improve the system, including recommendations on how these upgrades could advance the functionality and effectiveness of AI-based Smart Security Systems in smart home applications.

With the popularity of smart home services, smart home devices are also increasing significantly. At the same time, security problems of smart home services are becoming more and more important. With the characteristics of non-tampering and multi-party consensus mechanism, Blockchain technology provides powerful capabilities in security protection. In this paper, we introduce the widely used permission Blockchain Fabric for smart home services. As the high requirements of performance, we firstly study the methods of performance optimization for Fabric. Then, a Fabric-based smart home security control system is designed. Based on this system, the smart home system is able to provide access control and security control for smart home devices. The experimental results show that the system works best when the number of concurrent registrations of smart home device is under 6000.

When managing multiple smart homes and spaces with multiple stakeholders, key questions arise about creating an architecture that supports automation, encompassing aspects such as monitoring and control, ensuring privacy and security, and analysis and recommendation from shared accumulated data. To address those issues, we designed a rule language based on separating concerns, allowing users to specify desired outcomes without addressing technical implementation details. The separation of concerns resulted in a layered architecture that converts high-level decisions into home-specific actions, and sensor data into high-level information for decision making. A critical component, a rule engine, was designed and implemented. It evaluates and carries out the rules according to the state and context of the home and its residents, recognizing and resolving conflicting rules. A recommender system can suggest rules based on learning the residents’ behavior or by including rules of similar residents or households. A structured, policy-driven process addresses rule management issues: conflict resolution, security, privacy and distribution of authority. Our architecture supports seamless integration of rule or hardware changes and long-term data collection facilities, enabling learning residents’ behavior patterns to refine and automate decision-making. Article Highlights In this article, we examine the challenge of smart spaces, which typically involve multiple stakeholders with different levels of authority and responsibility. We analyze the problem from various perspectives and propose a comprehensive system architecture and design, incorporating novel algorithms to meet the requirements of such systems. In addition, we address issues related to roles and authority, user hierarchy, conflict detection and resolution, auto-negation, and full automation, where rules can be seamlessly applied across diverse environments.