Search Results (714)

This review examines advancements in smart home security through the integration of home automation technologies. Various security systems, including surveillance cameras, smart locks, and motion sensors, are analyzed, highlighting their effectiveness in enhancing home security. These systems enable users to monitor and control their homes in real-time, providing an additional layer of security. The document also examines how these security systems can enhance the quality of life for users by providing greater convenience and control over their domestic environment. The ability to receive instant alerts and access video recordings from anywhere allows users to respond quickly to unexpected situations, thereby increasing their sense of security and well-being. Additionally, the challenges and future trends in this field are addressed, emphasizing the importance of designing solutions that are intuitive and easy to use. As technology continues to evolve, it is crucial for developers and manufacturers to focus on creating products that seamlessly integrate into users’ daily lives, facilitating their adoption and use. This comprehensive state-of-the-art review, based on the Scopus database, provides a detailed overview of the current status and future potential of smart home security systems. It highlights how ongoing innovation in this field can lead to the development of more advanced and efficient solutions that not only protect homes but also enhance the overall user experience. Full article

The rapid increase in the global population of older adults presents a significant challenge, but also a unique opportunity to leverage technological advancements for promoting independent living and well-being. This study introduces the CIREI framework, which is a comprehensive model designed to enhance the integration of smart home and assistive technologies specifically for pre-frail older adults. Developed through a systematic literature review and innovative and comprehensive co-design activities, the CIREI framework captures the nuanced needs, preferences, and challenges faced by older adults, caregivers, and experts. Key findings from the co-design workshop highlight critical factors such as usability, privacy, and personalised learning preferences, which directly influence technology adoption. These insights informed the creation of an intelligent middleware prototype named WISE-WARE, which seamlessly integrates commercial off-the-shelf (COTS) devices to support health management and improve the quality of life for older adults. The CIREI framework’s adaptability ensures it can be extended and refined to meet the ever-changing needs of the ageing population, providing a robust foundation for future research and development in user-centred technology design. All workshop materials, including tools and methodologies, are made available to encourage the further exploration and adaptation of the CIREI framework, ensuring its relevance and effectiveness in the dynamic landscape of ageing and technology. This research contributes significantly to the discourse on ageing in place, digital inclusion, and the role of technology in empowering older adults to maintain independence. Full article

Japan’s 8.5 million vacant homes (Akiyas) represent a paradox of scarcity amid surplus: while rural depopulation leaves properties abandoned, housing shortages and bureaucratic inefficiencies hinder their reuse. This study proposes AKI2ALL, an AI-blockchain framework designed to automate the circular repurposing of Akiyas into ten high-value community assets—guesthouses, co-working spaces, pop-up retail and logistics hubs, urban farming hubs, disaster relief housing, parking lots, elderly daycare centers, exhibition spaces, places for food and beverages, and company offices—through smart contracts and data-driven workflows. By integrating circular economy principles with decentralized technology, AKI2ALL streamlines property transitions, tax validation, and administrative processes, reducing operational costs while preserving embodied carbon in existing structures. Municipalities list properties, owners select uses, and AI optimizes assignments based on real-time demand. This work bridges gaps in digital construction governance, proving that automating trust and accountability can transform systemic inefficiencies into opportunities for community-led, low-carbon regeneration, highlighting its potential as a scalable model for global vacant property reuse. Full article

With the increasing integration of renewable energy and smart technologies in residential energy systems, proactive household energy management (HEM) have become critical for reducing costs, enhancing grid stability, and achieving sustainability goals. This study proposes a ultra-short-term forecasting-driven proactive energy consumption optimization strategy that integrates advanced forecasting models with multi-objective scheduling algorithms. By leveraging deep learning techniques like Graph Attention Network (GAT) architectures, the system predicts ultra-short-term household load profiles with high accuracy, addressing the volatility of residential energy use. Then, based on the predicted data, a comprehensive consideration of electricity costs, user comfort, carbon emission pricing, and grid load balance indicators is undertaken. This study proposes an enhanced mixed-integer optimization algorithm to collaboratively optimize multiple objective functions, thereby refining appliance scheduling, energy storage utilization, and grid interaction. Case studies demonstrate that integrating photovoltaic (PV) power generation forecasting and load forecasting models into a home energy management system, and adjusting the original power usage schedule based on predicted PV output and water heater demand, can effectively reduce electricity costs and carbon emissions without compromising user engagement in optimization. This approach helps promote energy-saving and low-carbon electricity consumption habits among users. Full article

As the demand for energy-efficient homes continues to rise, the importance of advanced mechanical ventilation systems in maintaining indoor air quality (IAQ) has become increasingly evident. However, challenges related to energy balance, IAQ, and occupant thermal comfort persist. This review examines the performance of mechanical ventilation systems in regulating indoor climate, improving air quality, and minimising energy consumption. The findings indicate that demand-controlled ventilation (DCV) can enhance energy efficiency by up to 88% while maintaining CO₂ concentrations below 1000 ppm during 76% of the occupancy period. Heat recovery systems achieve efficiencies of nearly 90%, leading to a reduction in heating energy consumption by approximately 19%. Studies also show that employing mechanical rather than natural ventilation in schools lowers CO₂ levels by 20–30%. Nevertheless, occupant misuse or poorly designed systems can result in CO₂ concentrations exceeding 1600 ppm in residential environments. Hybrid ventilation systems have demonstrated improved thermal comfort, with predicted mean vote (PMV) values ranging from –0.41 to 0.37 when radiant heating is utilized. Despite ongoing technological advancements, issues such as system durability, user acceptance, and adaptability across climate zones remain. Smart, personalized ventilation strategies supported by modern control algorithms and continuous monitoring are essential for the development of resilient and health-promoting buildings. Future research should prioritize the integration of renewable energy sources and adaptive ventilation controls to further optimise system performance. Full article

Smart home technology enhances the quality of life, particularly with respect to in-home care and health monitoring. While video-based methods provide accurate behavior analysis, privacy concerns drive interest in non-visual alternatives. This study proposes a Bluetooth Low Energy (BLE)-enabled indoor positioning and behavior recognition system, integrating machine learning techniques to support sustainable and privacy-preserving health monitoring. Key optimizations include: (1) a vertically mounted Data Collection Unit (DCU) for improved height positioning, (2) synchronized data collection to reduce discrepancies, (3) Kalman filtering to smooth RSSI signals, and (4) AI-based RSSI analysis for enhanced behavior recognition. Experiments in a real home environment used a smart wristband to assess BLE signal variations across different activities (standing, sitting, lying down). The results show that the proposed system reliably tracks user locations and identifies behavior patterns. This research supports elderly care, remote health monitoring, and non-invasive behavior analysis, providing a privacy-preserving solution for smart healthcare applications. Full article

There is a growing pressure on industry to secure environments and demonstrate their commitment in taking right steps to secure their products. This is because of the growing number of security compromises in the IT industry, Operational Technology environment, Internet of Things environment and smart home devices. These compromises are not just about data breaches or data exfiltration, but also about unauthorised access to devices that are not configured correctly and vulnerabilities in software components, which usually lead to insecure authentication and authorisation. Incorrect configurations are usually in the form of devices being made available on the Internet (public domain), reusable credentials, access granted without verifying the requestor, and easily available credentials like default credentials. Organisations seeking to address the dual pressure of demonstrating steps in the right direction and addressing unauthorised access to resources can find a viable approach in the form of the zero trust concept. Zero trust principles are about moving security controls closer to the data, applications, assets and services and are based on the principle of “never trust, always verify”. As it stands today, zero trust research has advanced far beyond the concept of “never trust, always verify”. This paper provides the culmination of a literature review of research conducted in the space of smart home devices and IoT and the applicability of the zero trust five-step implementation process to secure them. We discuss the history of zero trust, the tenets of zero trust, the five-step implementation process for zero trust, and its adoption for smart home devices and Internet of Things, and we provide suggestions for future research. Full article

Despite notable advancements in smart home technologies, residential energy management continues to face critical challenges. These include the complex integration of intermittent renewable energy sources, issues related to data latency, interoperability, and standardization across diverse systems, the inflexibility of centralized control architectures in dynamic environments, and the difficulty of accurately modeling and influencing occupant behavior. To address these challenges, this study proposes an intelligent multi-agent system designed to accurately estimate and control energy consumption in residential buildings, with the overarching objective of optimizing energy usage while maintaining occupant comfort and satisfaction. The methodological approach employed is a hybrid framework, integrating multi-agent system architecture with system dynamics modeling and agent-based modeling. This integration enables decentralized and intelligent control while simultaneously simulating physical processes such as heat exchange, insulation performance, and energy consumption, alongside behavioral interactions and real-time adaptive responses. The system is tested under varying conditions, including changes in building insulation quality and external temperature profiles, to assess its capability for accurate control and estimation of energy use. The proposed tool offers significant added value by supporting real-time responsiveness, behavioral adaptability, and decentralized coordination. It serves as a risk-free simulation platform to test energy-saving strategies, evaluate cost-effective insulation configurations, and fine-tune thermostat settings without incurring additional cost or real-world disruption. The high fidelity and predictive accuracy of the system have important implications for policymakers, building designers, and homeowners, offering a practical foundation for informed decision making and the promotion of sustainable residential energy practices. Full article

The deep integration of smart grid and Internet of Things technologies has made high-speed power line carrier communication a key communication technology in energy management, industrial monitoring, and smart home applications, owing to its advantages of requiring no additional wiring and offering wide coverage. However, the inherent characteristics of power line channels, such as strong noise, multipath fading, and time-varying properties, pose challenges to traditional encryption algorithms, including low key distribution efficiency and weak anti-interference capabilities. These issues become particularly pronounced in high-speed transmission scenarios, where the conflict between data security and communication reliability is more acute. To address this problem, a secure transmission method for high-speed power line carrier communication based on generalized two-dimensional polynomial chaotic mapping is proposed. A high-speed power line carrier communication network is established using a power line carrier routing algorithm based on the minimal connected dominating set. The autoregressive moving average model is employed to determine the degree of transmission fluctuation deviation in the high-speed power line carrier communication network. Leveraging the complex dynamic behavior and anti-decoding capability of generalized two-dimensional polynomial chaotic mapping, combined with the deviation, the communication key is generated. This process yields encrypted high-speed power line carrier communication ciphertext that can resist power line noise interference and signal attenuation, thereby enhancing communication confidentiality and stability. By applying reference modulation differential chaotic shift keying and integrating the ciphertext of high-speed power line carrier communication, a secure transmission scheme is designed to achieve secure transmission in high-speed power line carrier communication. The experimental results demonstrate that this method can effectively establish a high-speed power line carrier communication network and encrypt information. The maximum error rate obtained by this method is 0.051, and the minimum error rate is 0.010, confirming its ability to ensure secure transmission in high-speed power line carrier communication while improving communication confidentiality. Full article

Bagheria, located on the northern coast of Sicily, is home to one of the Mediterranean’s most remarkable ensembles of Baroque villas, constructed between the 17th and 18th centuries by the aristocracy of Palermo. Originally situated within a highly structured rural landscape of citrus groves, gardens, and visual axes, these monumental residences have undergone substantial degradation due to uncontrolled urban expansion throughout the 20th century. This study presents a diachronic spatial analysis of Bagheria’s territorial transformation from 1850 to 2018, integrating historical cartography, aerial photography, satellite imagery, and Geographic Information System (GIS) tools. A total of 33 villas were identified, georeferenced, and assessed based on their spatial integrity, architectural condition, and relationship with the evolving urban fabric. The results reveal a progressive marginalization of the villa system, with many heritage assets now embedded within dense residential development, severed from their original landscape context and deprived of their formal gardens and visual prominence. Comparative insights drawn from analogous Mediterranean heritage landscapes, such as Ortigia (Siracusa), the Appian Way (Rome), and Athens, highlight the urgency of adopting integrated conservation frameworks that reconcile urban development with cultural and ecological continuity. As a strategic response, the study proposes the creation of a thematic cultural route, La città delle ville, to enhance the visibility, accessibility, and socio-economic relevance of Bagheria’s heritage system. This initiative, supported by adaptive reuse policies, smart heritage technologies, and participatory planning, offers a replicable model for sustainable territorial regeneration and heritage-led urban resilience. Full article

The growing mismanagement of energy resources is a pressing issue that poses significant risks to both individuals and the environment. As energy consumption continues to rise, the ramifications become increasingly severe, necessitating urgent action. In response, the rapid expansion of Internet of Things (IoT) devices offers a promising and innovative solution due to their adaptability, low power consumption, and transformative potential in energy management. This study describes a novel, integrative strategy that integrates IoT and Artificial Neural Networks (ANNs) in a smart monitoring mobile application intended to optimize energy usage and promote sustainability in residential settings. While both IoT and ANN technologies have been investigated separately in previous research, the uniqueness of this work is the actual integration of both technologies into a real-time, user-adaptive framework. The application allows for continuous energy monitoring via modern IoT devices and wireless sensor networks, while ANN-based prediction models evaluate consumption data to dynamically optimize energy use and reduce environmental effect. The system’s key features include simulated consumption scenarios and adaptive user profiles, which account for differences in household behaviors and occupancy patterns, allowing for tailored recommendations and energy control techniques. The architecture allows for remote device control, real-time feedback, and scenario-based simulations, making the system suitable for a wide range of home contexts. The suggested system’s feasibility and effectiveness are proved through detailed simulations, highlighting its potential to increase energy efficiency and encourage sustainable habits. This study contributes to the rapidly evolving field of intelligent energy management by providing a scalable, integrated, and user-centric solution that bridges the gap between theoretical models and actual implementation. Full article

Background/Objectives: Artificial intelligence (AI) offers opportunities to enhance healthcare accessibility for people with disabilities (PwDs). However, their application in Saudi Arabia remains limited. This study explores PwDs’ experiences with AI technologies within the Kingdom’s Vision 2030 digital health framework to inform inclusive healthcare innovation strategies. Methods: Semi-structured interviews were conducted with nine PwDs across Riyadh, Al-Jouf, and the Northern Border region between January and February 2025. Participants used various AI-enabled technologies, including smart home assistants, mobile health applications, communication aids, and automated scheduling systems. Thematic analysis following Braun and Clarke’s six-phase framework was employed to identify key themes and patterns. Results: Four major themes emerged: (1) accessibility and usability challenges, including voice recognition difficulties and interface barriers; (2) personalization and autonomy through AI-assisted daily living tasks and medication management; (3) technological barriers such as connectivity issues and maintenance gaps; and (4) psychological acceptance influenced by family support and cultural integration. Participants noted infrastructure gaps in rural areas, financial constraints, limited disability-specific design, and digital literacy barriers while expressing optimism regarding AI’s potential to enhance independence and health outcomes. Conclusions: Realizing the benefits of AI for disability healthcare in Saudi Arabia requires culturally adapted designs, improved infrastructure investment in rural regions, inclusive policymaking, and targeted digital literacy programs. These findings support inclusive healthcare innovation aligned with Saudi Vision 2030 goals and provide evidence-based recommendations for implementing AI healthcare technologies for PwDs in similar cultural contexts. Full article

As a fundamental technology in environmental perception, sound source localization (SSL) plays a critical role in public safety, marine exploration, and smart home systems. However, traditional methods such as beamforming and time-delay estimation rely on manually designed physical models and idealized assumptions, which struggle to meet practical demands in dynamic and complex scenarios. Recent advancements in deep learning have revolutionized SSL by leveraging its end-to-end feature adaptability, cross-scenario generalization capabilities, and data-driven modeling, significantly enhancing localization robustness and accuracy in challenging environments. This review systematically examines the progress of deep learning-based SSL across three critical domains: marine environments, indoor reverberant spaces, and unmanned aerial vehicle (UAV) monitoring. In marine scenarios, complex-valued convolutional networks combined with adversarial transfer learning mitigate environmental mismatch and multipath interference through phase information fusion and domain adaptation strategies. For indoor high-reverberation conditions, attention mechanisms and multimodal fusion architectures achieve precise localization under low signal-to-noise ratios by adaptively weighting critical acoustic features. In UAV surveillance, lightweight models integrated with spatiotemporal Transformers address dynamic modeling of non-stationary noise spectra and edge computing efficiency constraints. Despite these advancements, current approaches face three core challenges: the insufficient integration of physical principles, prohibitive data annotation costs, and the trade-off between real-time performance and accuracy. Future research should prioritize physics-informed modeling to embed acoustic propagation mechanisms, unsupervised domain adaptation to reduce reliance on labeled data, and sensor-algorithm co-design to optimize hardware-software synergy. These directions aim to propel SSL toward intelligent systems characterized by high precision, strong robustness, and low power consumption. This work provides both theoretical foundations and technical references for algorithm selection and practical implementation in complex real-world scenarios. Full article

Stroke-induced hand dysfunction substantially impairs patients’ quality of life, creating an urgent need for portable, adaptive rehabilitation devices. This study introduces a smart rehabilitation glove actuated by shape-memory alloy (SMA) wires, leveraging their high power-to-weight ratio, controllable strain recovery, and reversible phase transformation to overcome the limitations of conventional motor-driven or pneumatic gloves. The glove incorporates SMA-based actuation units achieving 50 mm contraction (5% strain) within 7 s, enabling finger flexion to ~34° for personalized rehabilitation protocols. A mobile application provides wireless regulation of SMA actuation modes and facilitates real-time telemedicine consultations. The prototype demonstrates an ultra-lightweight, compact design enabled by SMA’s intrinsic properties, offering a promising solution for home-based post-stroke rehabilitation. This work establishes the transformative potential of SMAs in wearable biomedical technologies. Full article

The acceleration of digitalization has caused an increase in demand for autonomous devices. In this paper, the technologies of artificial intelligence (AI), and especially machine learning (ML), integrated into applications that use self-powered Internet of Things (IoT) sensors are analyzed. The study addresses the issue of the lack of a standardized classification of IoT domains and the uneven distribution of AI integration in these domains. The systematic bibliometric analysis of the scientific literature between 1 January 2020 and 30 April 2025, using the Web of Science database, outlines the seven main areas of IoT sensor usage: smart cities, wearable devices, industrial IoT, smart homes, environmental monitoring, healthcare IoT, and smart mobility. The thematic searches highlight the consistent number of articles in the health sector and the underrepresentation of other areas, such as agriculture. The study identifies that the most commonly used sensors are the accelerometer, electrocardiogram, humidity sensor, motion sensor, and temperature sensor, and analyzes the performance of AI models in self-powered systems, identifying accuracies that can reach up to 99.92% in medical and industrial applications. The conclusions drawn from these results underscore the need for an interdisciplinary approach and detailed exploration of ML algorithms to be adapted to the hardware infrastructures of autonomous sensors. The paper proposes future research directions to expand AI’s applicability in developing systems that integrate self-powered IoT sensors. The paper lays the groundwork for future projects in this field, serving as a reference for researchers who wish to explore these areas. Full article