Search Results (797)

The rapid development of Internet of Things technologies has accelerated the deployment of smart home systems. However, perception solutions based on visual sensors remain constrained by illumination sensitivity, occlusion, and privacy concerns. Frequency-modulated continuous-wave (FMCW) millimeter-wave radar provides a promising alternative because it operates independently of lighting conditions, is robust to environmental changes, and preserves user privacy. To address multiple-extended-target tracking in cluttered indoor environments, this paper proposes a high-accuracy tracking algorithm that combines an improved Density-Based Spatial Clustering of Applications with Noise (DBSCAN) algorithm, an optimized Nearest-Neighbor Data Association (NNDA) scheme, and an Extended Kalman Filter (EKF). The improved DBSCAN algorithm introduces spatial-extent constraints, velocity-consistency checks, and candidate-cluster validation to cluster raw radar point clouds and convert extended targets into representative point targets with little additional computational cost. The optimized NNDA scheme then integrates clustering information into the association process, improving the matching accuracy between existing tracks and current measurements. Finally, the EKF estimates the state of each target from the associated measurements. Real-world experiments show that the proposed algorithm achieves tracking errors below 0.4 m in typical motion scenarios, maintains continuous tracking in two-person crossing scenarios, and reaches 93.3% counting accuracy in five-person scenarios. These results outperform the tracking system based on the commercial Texas Instruments (TI) IWR6843ISK millimeter-wave radar evaluation board. The proposed method offers a reliable and privacy-preserving sensing solution for smart homes, elderly care, and intelligent building applications. Full article

Poor indoor air quality, inadequate ventilation, and unnoticed local disturbances can reduce occupant well-being and compromise practical safety in smart-home and small-building environments. Although low-cost Internet-of-Things (IoT) sensing technologies are widely available, many monitoring systems remain focused on single-modality sensing and do not jointly evaluate environmental conditions, vibration activity, communication reliability, and gateway-side interpretation within one framework. This study presents the design, implementation, and proof-of-concept evaluation of a low-cost, privacy-conscious, non-imaging IoT-based indoor environment and safety-awareness monitoring framework built with ESP32/Arduino sensor nodes and a Raspberry Pi gateway. The system integrates carbon dioxide, temperature, humidity, gas-resistance/VOC-trend indication, and vibration sensing with MQTT-based communication and edge-side analytics. Controlled subsystem experiments showed that CO₂ concentration differentiated ventilation conditions, increasing from 395.47 ppm in the valid empty/open-door baseline to 1083.16 ppm in the closed occupied condition. Vibration states were distinguished using root-mean-square acceleration features across calm, surface-disturbance, footstep, play, and jump conditions. MQTT evaluation using 1000-message batches showed no observed message loss or duplicates across the tested QoS/network combinations, although latency and throughput varied by network configuration and QoS level. QoS 1 provided a practical balance between low latency and protocol-level delivery assurance in the tested local/Wi-Fi setting. A final integrated validation run further demonstrated synchronized acquisition from indoor environmental, vibration, and outdoor CO₂ reference publishers through the same Raspberry Pi gateway, with zero missing or duplicate sequence flags across the three streams. Overall, the findings indicate that lightweight open-source IoT hardware can support a reproducible building-level sensing and edge-analytics prototype for indoor environment and safety-awareness monitoring. Broader deployment in standard-sized rooms, multi-room buildings, and smart-city infrastructure remains future work. Full article

The increasing integration of renewable energy sources, smart appliances, and distributed energy technologies has significantly increased the complexity of residential energy systems, necessitating advanced Home Energy Management Systems (HEMS). Optimization techniques play a critical role in achieving key objectives, including energy cost reduction, load balancing, minimizing the peak-to-average ratio, and enhancing user comfort. This paper presents a comprehensive review and critical analysis of optimization techniques employed in HEMS, including mathematical methods, metaheuristic algorithms, artificial intelligence (AI)-based approaches, and rule-based strategies. These techniques are systematically classified and compared based on scalability, computational complexity, uncertainty handling, and real-time applicability. The analysis reveals that while conventional methods provide reliable solutions for structured problems, AI-based techniques offer superior adaptability and performance in dynamic and data-driven environments. Furthermore, key research gaps are identified, including limited multi-objective optimization, inadequate consideration of uncertainty and electric vehicle integration, and the lack of real-world implementation. Finally, future research directions are outlined, emphasizing hybrid optimization frameworks and intelligent, IoT-enabled energy management systems. Full article

Human activity recognition (HAR) using Wi-Fi channel state information (CSI) offers a privacy-preserving and contactless sensing modality suitable for smart homes, healthcare monitoring, and pervasive mobile Internet of Things (IoT) environments. However, existing CSI-based HAR approaches often suffer from computational inefficiency, high latency, and limited feasibility on resource-constrained embedded platforms. This work presents STAR (Sensing Technology for Activity Recognition), an edge AI-optimized framework that integrates lightweight temporal modeling, adaptive signal processing, and hardware-aware co-optimization to enable real-time, energy-efficient HAR on low-power embedded devices. STAR employs a streamlined three-layer Gated Recurrent Unit (GRU) architecture that reduces model parameters by 33% compared to conventional Long Short-Term Memory (LSTM) designs while maintaining strong temporal modeling capability. To enhance signal quality, STAR incorporates a multi-stage pre-processing pipeline consisting of median filtering, an eighth-order Butterworth low-pass filtering, and empirical mode decomposition (EMD) to denoise CSI amplitude measurements and extract stable spatial-temporal features. For on-device deployment, the system is implemented on a Rockchip RV1126 processor equipped with an embedded Neural Processing Unit (NPU) and interfaced with an ESP32-S3 CSI acquisition module. Experimental results demonstrate a mean recognition accuracy of 93.52% across seven activity classes and 99.11% for human-presence detection using a compact 97.6k-parameter model. INT8-quantized inference achieves a processing throughput of 33 MHz with only 8% CPU utilization, achieving a six-fold improvement in inference speed over CPU-based execution. With sub-second response latency and low power consumption, the system ensures real-time, privacy-preserving HAR, offering a practical, scalable solution for mobile and pervasive computing environments. Full article

Osteoarthritis (OA) is a highly prevalent musculoskeletal disorder and a major cause of disability, posing growing challenges for healthcare systems worldwide. Conventional supervised clinical assessments provide valuable insights but are largely limited to cross-sectional snapshots and often fail to reflect the variability of real-world functioning, physical activity patterns, and symptom fluctuations experienced by individuals with OA, especially those with knee OA. This perspective introduces a multisensor digital phenotyping framework for smart knee OA assessment, integrating supervised laboratory evaluations with unsupervised continuous monitoring in daily living environments using wearable sensors, smart insoles, activity trackers, and mobile devices. Feasibility was tested in 40 participants (20 knee OA patients, 20 controls). Raw data from questionnaires, electronic goniometry, dynamometry, force plate, connected insoles, and seven-day home monitoring were harmonized via a standardized pipeline aligned with the ICF framework. The pipeline employed anomaly detection, missing data imputation, z-score normalization, and cloud-based storage. This framework is envisioned to facilitate advanced data integration and machine-learning-ready analytics, enabling longitudinal monitoring, pattern recognition, and individualized health profiling. By conceptually bridging cross-sectional and continuous sensing modalities, this approach has the potential to enhance ecological validity, support earlier identification of functional decline, and inform data-driven clinical decision-making. Key methodological, technological, and ethical challenges—including data quality, interpretability, privacy, digital literacy, and clinical adoption—are also highlighted. Overall, this paper underscores the promise of AI-enabled multisensor digital phenotyping to advance smart, personalized, and precision healthcare for individuals with knee OA. Full article

The urban housing sector plays a significant role in global energy consumption and carbon emissions, making the sustainable transformation of domestic buildings essential to achieving climate goals. Urban housing is also linked to the energy transition, social equity, public health, and environmental resilience. The UK’s Warm Homes Plan (WHP) is seen as a key policy initiative that aims to improve energy efficiency and living conditions, and to promote the transition to a low-carbon future. This study provides an integrated review of retrofit assessment, policy mechanisms, and socio-environmental factors in the context of urban housing decarbonization. This study adopts a structured critical review approach to analyze retrofit strategies, low-carbon heating systems, renewable energy integration, and smart control technologies. The study highlights that retrofit assessment is not limited to technical performance but also includes social acceptability, affordability, and urban infrastructure compatibility. Furthermore, case study comparisons show that decarbonization outcomes are improved when technical measures are integrated with effective governance, stakeholder engagement, and local policy support. This study presents an integrated conceptual framework that links technical retrofit measures, policy coordination, and socio-environmental indicators. The results show that isolated technical solutions are insufficient for decarbonizing urban housing. Rather, a multi-dimensional planning approach is necessary to enable a sustainable, resilient, and socially inclusive housing transition. Full article

Background: Although the application of smart home technology in the Chinese eldercare market is widespread, its effectiveness from the users’ perspective remains underexplored. This qualitative study examines the perceptions and experiences of older adult users in adopting and applying the Care-on-Call services (Ping An Tong; PAT), a prominent example of smart home technology for eldercare in Mainland China. Methods: Individual and dyadic interviews were conducted with 28 older adult users from diverse physical, socioeconomic, and familial backgrounds. Thematic analysis was performed. Results: Two overarching themes were illustrated based on thematic analysis. First, the multifaceted challenges of using PAT encompass an incomplete cognition of the services, unfamiliarity with PAT systems, psycho-cultural resistance, ‘do it yourself, don’t bother others’, economic concerns of additional costs, and ethical concerns regarding information security and privacy. Second, bridging the technology divide highlights the empowerment of PAT use among older adults through a variety of educational methods to effectively utilize the services, enhancing service effectiveness through the integration of smart home technology and social service provision, and increasing service accessibility through inclusive services. The disparities in smart home technology application between China and the West are also discussed. Conclusions: Psychosocial support, organizational programs, and the integrated service model are recommended to promote the utilization of smart home technology among older adults in China. Full article

Residential buildings are a major source of urban carbon emissions, yet the uptake of smart home retrofitting remains far below the level required to meet decarbonization and sustainability targets. While technical solutions for energy-efficient renovation are well established, less is known about how behavioral, psychological, and institutional factors jointly shape household retrofit decisions and their broader sustainability implications. This study develops an integrated analytical framework that combines UTAUT2 with perceived risk, trust, innovativeness, and regulatory pressure, interpreted through a socio-technical systems perspective, to examine smart home retrofitting in Thailand and its contribution to Sustainable Community Development Goals (SCDG). Survey data were collected from 448 households in Bangkok and Chonburi and analyzed using structural equation modeling. The results show that traditional UTAUT2 predictors such as performance expectancy, effort expectancy, and social influence do not significantly influence adoption intention in this high-cost retrofit context. Instead, innovativeness, trust, price value, perceived risk, and regulatory pressure emerge as key behavioral and institutional drivers, while facilitating conditions and habits shape actual use behavior. Actual retrofit behavior is found to generate significant economic, environmental, socio-cultural, technological, and public-policy sustainability outcomes aligned with SCDG. These findings demonstrate the limitations of conventional technology acceptance models in infrastructure-based contexts and provide a mechanism-based explanation of how retrofit adoption is driven in high-cost sustainability contexts. Full article

The Internet of Things (IoT) has a decentralized smart home ecosystem, as each protocol has its own gateway infrastructure needs. This study advances gateway convergence by proposing and rigorously evaluating a scalable architectural framework for future smart-home infrastructure. Specifically, this paper provides a detailed analysis of a proposed integrated multi-protocol gateway design that supports 18 of the most widely used IoT communication protocols simultaneously. It is a one-device implementation combining wireless technologies, including short-range radios (Sub-1 GHz, 2.4 GHz), LPWANs (Long Power Wide Area Networks), cellular (LTE, Long-Term Evolution), and wired (Ethernet, KNX). Using the ns-3 network simulator, this paper shows that this architecture is practical in a simulated smart-home environment with a large number of interconnected devices distributed across various zones. The results demonstrate substantial reductions in energy consumption and operational complexity, without compromising quality of service across heterogeneous communication technologies. Full article

The widespread deployment of Internet of Things (IoT) technologies in smart-home energy systems has increased the demand for secure, context-aware, and energy-efficient access control (AC) mechanisms. Although blockchain-based AC provides immutability, auditability, and fine-grained policy enforcement, its dependence on on-chain decision-making introduces significant computational latency and energy overhead, limiting its suitability for resource-constrained IoT environments. This paper proposes Optimized Dynamic-Attribute-Based Access Control-IoT (ODABAC-IoT), a hybrid off-chain and decentralized ABAC framework that combines off-chain LightGBM inference with selective on-chain verification to reduce blockchain workload while preserving trust and transparency. This work focuses on improving the computational efficiency, latency, and energy consumption of blockchain-enabled AC within smart-home energy systems, rather than directly optimizing physical energy consumption. In the proposed framework, high-confidence access requests are evaluated off-chain, whereas uncertain requests are forwarded to smart contracts for final validation. This hybrid decision-making strategy reduces unnecessary blockchain transactions, lowers latency, and improves computational efficiency without compromising security. Experimental results demonstrate up to 65% reduction in blockchain transaction volume, 64% improvement in latency, and 65% reduction compared to on-chain ABAC and 50% compared to hybrid blockchain approaches. These gains correspond to a reduction in daily blockchain energy consumption from 10 kWh to 3.5 kWh in a representative household scenario. The results indicate that ODABAC-IoT improves scalability, energy efficiency of the digital control layer, and responsiveness in IoT-enabled smart home energy systems, offering an effective pathway toward energy-aware and secure AC in the digital infrastructure of smart home energy systems. Full article

Threat modelling is a methodology employed for identifying and analysing threats and applicable mitigations for web applications, mobile applications, infrastructure, and environments including smart home environments. Threat modelling starts with a tabletop exercise to identify threats. It provides extremely important insights into what can go wrong if certain events or a series of events take place. The identification of these events is critical to ensuring the right mitigation strategies are applied. Threat modelling also helps to identify security controls that may be assumed to provide required security, but, in reality, may not be addressing the existing and applicable threat(s). Existing literature, in the public domain and in academia, discusses threat materialisation for smart homes; however, entry points for a threat to materialise and exploit these vulnerabilities are not explored and a dedicated threat model for smart home environments is currently unavailable. Whilst threats can be mitigated by smart home device manufacturers, there are also mitigations that need to be applied by smart home owners who are both technology-aware and technology-unaware. In this paper, we propose a structured, domain-specific threat modelling methodology for smart home environments. The methodology models threats from a smart home owner’s perspective, identifies entry points and the mitigations that need to be implemented by a smart home owner. It also acknowledges that the attack surface expands and contracts and is not constant; which is addressed by applying zero-trust principles. Full article

Ambient Assisted Living (AAL) systems have become increasingly relevant as aging populations intensify the demand for technologies that promote autonomy, safety, and quality of life. However, the widespread adoption of audiovisual sensing in smart homes raises critical concerns regarding data protection, privacy, and user trust. Ensuring secure processing while maintaining accurate activity recognition remains a key challenge. This work introduces DistSense, a distributed Peer-to-Peer (P2P) system designed to enhance activity detection in domestic environments through collaborative inference among intelligent audiovisual sensors. DistSense prioritizes privacy by performing local processing, sharing only high-level events, and leveraging distributed ledger mechanisms to ensure data integrity and auditability and support cross-device validation. This collaborative strategy reduces false positives caused by occlusions, illumination variability, and acoustic noise. To assess the system, functional tests were conducted for each module, followed by two use cases evaluated in both simulated and real edge hardware environments. The trained models achieved 88% accuracy for audio and 80% for video, and the system demonstrated effective performance in detecting daily activities and domestic hazards under varying noise conditions. Results indicate that DistSense successfully balances security, user acceptance, and inference robustness, positioning it as a viable solution for privacy-preserving activity monitoring in smart home contexts. Full article

The growing prevalence of chronic diseases, population aging, and the shift toward preventive and personalized care under Healthcare 5.0 have increased the need for continuous health monitoring beyond clinical settings. While wearable devices enable remote monitoring, their long-term use is often limited by user compliance, comfort issues, battery dependence, and disruption of daily routines. To address these limitations, unobtrusive home-based health monitoring systems have emerged, integrating sensing technologies into domestic environments and everyday objects. This review provides a system-level analysis of unobtrusive health monitoring technologies for smart homes. It examines seven major sensing approaches, including camera-, laser-, radar-, infrared-, mechanical-, bioelectrical-, and optical-based sensors, and their integration into four home environments: living areas, bathrooms, bedrooms, and home offices. For each sensing modality, the operating principles, monitored physiological parameters, representative applications, and key advantages and limitations are discussed. Overall, existing solutions reveal trade-offs among measurement accuracy, robustness in real home conditions, energy autonomy, privacy preservation, and user acceptance. Heart rate and respiratory rate are the most commonly monitored parameters, while multimodal and clinically validated systems remain limited. Although unobtrusive sensing technologies show strong potential for proactive and personalized healthcare, challenges related to accuracy, interoperability, privacy, and cost continue to hinder large-scale adoption. Full article

Home automation is undergoing a paradigm shift from connected IoT environments with rule-based control to intelligent homes exhibiting ambient intelligence and proactive adaptation. Artificial intelligence, privacy-preserving sensing, and converging connectivity standards are the primary forces driving this transition. This systematic literature review synthesizes the technological foundations, architectural developments, emerging paradigms, and socio-technical challenges characterizing the next generation of smart homes, evaluated against the original Ambient Intelligence (AmI) vision. Following PRISMA 2020 guidelines, searches were conducted across four databases—IEEE Xplore, ACM Digital Library, Scopus, and Web of Science—covering studies published between January 2020 and June 2025. From 3450 records, 113 studies were selected through a two-reviewer screening procedure with inter-rater reliability assessments. Quality was assessed using a modified JBI Critical Appraisal Checklist, and findings were synthesized through thematic analysis. Three converging technological pillars were identified: multi-modal privacy-preserving sensing including mmWave radar; a hierarchical cloud-edge-TinyML intelligence engine; and unified connectivity through the Matter/Thread standard. Emerging paradigms include LLM-based cognitive orchestration, hyper-personalization, Digital Twin simulation, and grid-interactive prosumer energy management. Realizing that the intelligent home vision requires addressing the privacy–security–trust trilemma, algorithmic bias, system reliability, and human–agent collaboration, a research roadmap encompassing explainable AI, privacy-by-design, lifelong learning, and standardized ethical auditing is proposed. Full article