Security Evaluation of Companion Android Applications in IoT: The Case of Smart Security Devices.

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.

Industry visions of technology for older adults: A futures anthropology perspective.

The negative portrayal of ageing as a human decline burdening society has prompted Ageing Technology industries (AgeTech) to foresee solutions rooted in the Ageing in Place paradigm. These ostensibly neutral future interventions are intertwined with socio-technical dynamics. While Science and Technology Studies (STS) and anthropology scholars have questioned these AgeTech practices, limited literature explores industry's predictions of future AgeTech. Drawing on STS and futures-anthropology literature, I interrogate AgeTech industry visions of future assemblages involving older people, smart home technology, and socio-material discourses rooted in their own discrepancies and dilemmas. To unpack AgeTech futures, my methods include a review of 49 industry reports and 29 interviews with industry experts. Based on the reports, I designed comics to be used in interviews with experts spanning CEOs and managers of companies designing technology for older people, consultants, and aged-care workers based in 12 countries. Ageing futures are far from being neutral or a chronological process, instead they are non-consensual and fragmented. In the review and interviews, I captured future assemblages of a fragmented AgeTech industry in relationships with governments and industry giants. The fragmentation continues unfolding in participants from diverse countries and professions contesting dominant AgeTech narratives. In dissecting future assemblages, I also unpack non-consensual futures based on diverging experts' values (e.g. safety versus activity) and humans' values like control and improvisation challenging predictive and surveillance technology. AgeTech Futures transcend physical matters or assemblages of technologies and humans. They encompass future normativities, tensions, divergent values, and ideological concepts. I propose not only alternatives to the visions found in industry narratives, but also encourage scholars to understand the AgeTech industry's dilemmas.

Hybrid computing framework security in dynamic offloading for IoT-enabled smart home system.

In the distributed computing era, cloud computing has completely changed organizational operations by facilitating simple access to resources. However, the rapid development of the IoT has led to collaborative computing, which raises scalability and security challenges. To fully realize the potential of the Internet of Things (IoT) in smart home technologies, there is still a need for strong data security solutions, which are essential in dynamic offloading in conjunction with edge, fog, and cloud computing. This research on smart home challenges covers in-depth examinations of data security, privacy, processing speed, storage capacity restrictions, and analytics inside networked IoT devices. We introduce the Trusted IoT Big Data Analytics (TIBDA) framework as a comprehensive solution to reshape smart living. Our primary focus is mitigating pervasive data security and privacy issues. TIBDA incorporates robust trust mechanisms, prioritizing data privacy and reliability for secure processing and user information confidentiality within the smart home environment. We achieve this by employing a hybrid cryptosystem that combines Elliptic Curve Cryptography (ECC), Post Quantum Cryptography (PQC), and Blockchain technology (BCT) to protect user privacy and confidentiality. Additionally, we comprehensively compared four prominent Artificial Intelligence anomaly detection algorithms (Isolation Forest, Local Outlier Factor, One-Class SVM, and Elliptic Envelope). We utilized machine learning classification algorithms (random forest, k-nearest neighbors, support vector machines, linear discriminant analysis, and quadratic discriminant analysis) for detecting malicious and non-malicious activities in smart home systems. Furthermore, the main part of the research is with the help of an artificial neural network (ANN) dynamic algorithm; the TIBDA framework designs a hybrid computing system that integrates edge, fog, and cloud architecture and efficiently supports numerous users while processing data from IoT devices in real-time. The analysis shows that TIBDA outperforms these systems significantly across various metrics. In terms of response time, TIBDA demonstrated a reduction of 10-20% compared to the other systems under varying user loads, device counts, and transaction volumes. Regarding security, TIBDA's AUC values were consistently higher by 5-15%, indicating superior protection against threats. Additionally, TIBDA exhibited the highest trustworthiness with an uptime percentage 10-12% greater than its competitors. TIBDA's Isolation Forest algorithm achieved an accuracy of 99.30%, and the random forest algorithm achieved an accuracy of 94.70%, outperforming other methods by 8-11%. Furthermore, our ANN-based offloading decision-making model achieved a validation accuracy of 99% and reduced loss to 0.11, demonstrating significant improvements in resource utilization and system performance.

Analysis of In-Home Movement Patterns for Depression Assessment in Older Adults - A Feasibility Study.

Depression significantly impacts the wellbeing of older Australians, posing considerable challenges to their overall quality of life. This study aimed to detect in-home movement patterns of participants that could be indicative of depressive states. Utilising data collected over a 12-month period via smart home ambient sensors, this feasibility study conducted a comparative analysis using machine learning techniques on features derived from motion sensors, sociodemographic variables, and the Geriatric Depression Scale. Three machine learning models, specifically Extreme Gradient Boost (XGBoost), Random Forest (RF), and Logistic Regression (LR), were implemented. Results showed that the performance of XGBoost was relatively higher compared to RF and LR, with an Area Under the Receiver Operating Characteristic Curve (AUROC) value of 0.67. Feature analysis indicated that bathroom and kitchen movements and the level of home care support were among the top influential features influencing depression assessment. This is consistent with clinical evidence on appetite, hygiene, and overall mobility changes during depression. These findings underscore the feasibility of leveraging in-home movement monitoring as an indicator of health risks among older adults.

Software-Defined-Networking-Based One-versus-Rest Strategy for Detecting and Mitigating Distributed Denial-of-Service Attacks in Smart Home Internet of Things Devices.

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.

Unobtrusive Monitoring of Clinical Deterioration in Smart Homes.

Clinical deterioration (CD) is the physiological decompensation that incurs care escalation, protracted hospital stays, or even death. The early warning score (EWS) calculates the occurrence of CD based on five vital signs. However, there are limited reports regarding EWS monitoring in smart home settings. This study aims to design a CD detection system for health monitoring at home (HM@H) that automatically identifies unstable vital signs and alarms the medical emergency team. We conduct a requirement analysis by interviewing experts. We use unified modeling language (UML) diagrams to define the behavioral and structural aspects of HM@H. We developed a prototype using a SQL-based database and Python to calculate the EWS in the front end. A team of five experts assessed the accuracy and validity of the designed system. The requirement analysis for four main users yielded 30 data elements and 10 functions. Three main components of HM@H are the graphical user interface (GUI), the application programming interface (API), and the server. Results show the possibility of using unobtrusive sensors to collect smart home residents' vital signs and calculate their EWS scores in real-time. However, further implementation with real data, for frail elderly and hospital-discharged patients is required.

Co-creating Humanistic AI AgeTech to Support Dynamic Care Ecosystems: A Preliminary Guiding Model.

As society rapidly digitizes, successful aging necessitates using technology for health and social care and social engagement. Technologies aimed to support older adults (e.g., smart homes, assistive robots, wheelchairs) are increasingly applying artificial intelligence (AI), and thereby creating ethical challenges to technology development and use. The international debate on AI ethics focuses on implications to society (e.g., bias, equity) and to individuals (e.g., privacy, consent). The relational nature of care, however, warrants a humanistic lens to examine how "AI AgeTech" will shape, and be shaped by, social networks or care ecosystems in terms of their care actors (i.e., older adults, care partners, service providers); inter-actor relations (e.g., care decision-making) and relationships (e.g., social, professional); and evolving care arrangements. For instance, if an older adult's reduced functioning leads actors to renegotiate their risk tolerances and care routines, smart homes or robots become more than tools that actors configure; they become semi-autonomous actors, in themselves, with the potential to influence functioning and interpersonal relationships. As an experientially-diverse, transdisciplinary working group of older adults, care partners, researchers, clinicians, and entrepreneurs, we co-constructed intersectional care experiences, to guide technology research, development, and use. Our synthesis contributes a preliminary guiding model for AI AgeTech innovation that delineates humanistic attributes, values, and design orientations, and captures the ethical, sociological, and technological nuances of dynamic care ecosystems. Our visual probes and recommended tools and techniques offer researchers, developers/innovators, and care actors concrete ways of using this model to promote successful aging in AI-enabled futures.

Smart homes: pioneering age-friendly environments in China for enhanced health and quality of life.

Traditionally, China has been more reliant on a model of care that ensures older adults are cared for by family members. Whilst promoting the idea of older adults ageing in their own homes is essential, the provision of in-home care must shift from primarily relying on family caregivers to a model that places greater emphasis on gerontechnologies and enhanced healthcare service delivery. In this perspective article we argue for the adoption of a 'smart home' model in aged care in China. The smart home model argues for innovative technologies to older adult care, such as virtual support groups, video-conferencing, and electronic health records; assistive technologies that can safely maintain independence and assist with daily living such as sensors, wearables, telehealth, smart home technologies as well as interactive robotic technologies for mobility and cognitive support such as humanoid robots, rehabilitation robots, service/companion robots. The adoption and implementation of gerontechnologies have been slow, with only a handful of solutions demonstrating proven effectiveness in supporting home care. The utilisation of such digital technologies to support and enable older adults in China to age-in-place can bring a significant contribution to healthy ageing. Nonetheless, it's crucial to focus on co-creating with end-users, incorporating their values and preferences, and enhancing training to boost the adoption of these gerontechnologies. Through a smart home model of care, China can age-in-place more effectively, leading to significant contributions to healthy ageing.

Dynamically crosslinked chiral optics sensing for ultra-sensitive VOCs detection.

Chiroptical sensing with real-time colorimetrical detection has been emerged as quantifiable properties, enantioselective responsiveness, and optical manipulation in environmental monitoring, food safety and other trace identification fields. However, the sensitivity of chiroptical sensing materials remains an immense challenge. Here, we report a dynamically crosslinking strategy to facilitate highly sensitive chiroptical sensing material. Chiral nematic cellulose nanocrystals (CNC) were co-assembled with amino acid by a two-step esterification, of which a precisely tunable helical pitch, a unique spiral conformation with hierarchical and numerous active sites in sensing performance could be trigged by dynamic covalent bond on amines. Such a CNC/amino acid chiral optics features an ultra-trace amount of 0.08 mg/m3 and a high sensitivity of 60 nm/(mg/m3) for formaldehyde gas at a molecule level detection, which is due to the three synergistic adsorption enhancement of dynamic covalent bonded interaction, hydrogen bonded interaction and van der Waals interaction. Meanwhile, an enhancement hierarchical adsorption of CNC/amino acid chiral materials can be readily representative to the precise helical pitch and colorimetrical switch for sensitive visualization reorganization.

Using Graphs to Perform Effective Sensor-Based Human Activity Recognition in Smart Homes.

There has been a resurgence of applications focused on human activity recognition (HAR) in smart homes, especially in the field of ambient intelligence and assisted-living technologies. However, such applications present numerous significant challenges to any automated analysis system operating in the real world, such as variability, sparsity, and noise in sensor measurements. Although state-of-the-art HAR systems have made considerable strides in addressing some of these challenges, they suffer from a practical limitation: they require successful pre-segmentation of continuous sensor data streams prior to automated recognition, i.e., they assume that an oracle is present during deployment, and that it is capable of identifying time windows of interest across discrete sensor events. To overcome this limitation, we propose a novel graph-guided neural network approach that performs activity recognition by learning explicit co-firing relationships between sensors. We accomplish this by learning a more expressive graph structure representing the sensor network in a smart home in a data-driven manner. Our approach maps discrete input sensor measurements to a feature space through the application of attention mechanisms and hierarchical pooling of node embeddings. We demonstrate the effectiveness of our proposed approach by conducting several experiments on CASAS datasets, showing that the resulting graph-guided neural network outperforms the state-of-the-art method for HAR in smart homes across multiple datasets and by large margins. These results are promising because they push HAR for smart homes closer to real-world applications.

Integration of Smart Home and Building Automation Systems in Virtual Reality and Robotics-Based Technological Environment for Neurorehabilitation: A Pilot Study Protocol.

Technological innovation has revolutionized healthcare, particularly in neurological rehabilitation, where it has been used to address chronic conditions. Smart home and building automation (SH&BA) technologies offer promising solutions for managing chronic disabilities associated with such conditions. This single group, pre-post longitudinal pilot study, part of the H2020 HosmartAI project, aims to explore the integration of smart home technologies into neurorehabilitation. Eighty subjects will be enrolled from IRCCS San Camillo Hospital (Venice, Italy) and will receive rehabilitation treatment through virtual reality (VR) and robotics devices for 15 h per day, 5 days a week for 3 weeks in the HosmartAI Room (HR), equipped with SH&BA devices measuring the environment. The study seeks to optimize patient outcomes and refine rehabilitation practices. Findings will be disseminated through peer-reviewed publications and scientific meetings, contributing to advancements in neurological rehabilitation and guiding future research.

Efficient Connectivity in Smart Homes: Enhancing Living Comfort through IoT Infrastructure.

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.

HomeOSD: Appliance Operating-Status Detection Using mmWave Radar.

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 Cooperation Between Nurses and a New Digital Colleague "AI-Driven Lifestyle Monitoring" in Long-Term Care for Older Adults: Viewpoint.

Technology has a major impact on the way nurses work. Data-driven technologies, such as artificial intelligence (AI), have particularly strong potential to support nurses in their work. However, their use also introduces ambiguities. An example of such a technology is AI-driven lifestyle monitoring in long-term care for older adults, based on data collected from ambient sensors in an older adult's home. Designing and implementing this technology in such an intimate setting requires collaboration with nurses experienced in long-term and older adult care. This viewpoint paper emphasizes the need to incorporate nurses and the nursing perspective into every stage of designing, using, and implementing AI-driven lifestyle monitoring in long-term care settings. It is argued that the technology will not replace nurses, but rather act as a new digital colleague, complementing the humane qualities of nurses and seamlessly integrating into nursing workflows. Several advantages of such a collaboration between nurses and technology are highlighted, as are potential risks such as decreased patient empowerment, depersonalization, lack of transparency, and loss of human contact. Finally, practical suggestions are offered to move forward with integrating the digital colleague.

Semi-supervised ensemble learning for human activity recognition in casas Kyoto dataset.

-The automatic identification of human physical activities, commonly referred to as Human Activity Recognition (HAR), has garnered significant interest and application across various sectors, including entertainment, sports, and notably health. Within the realm of health, a myriad of applications exists, contingent upon the nature of experimentation, the activities under scrutiny, and the methodology employed for data and information acquisition. This diversity opens doors to multifaceted applications, including support for the well-being and safeguarding of elderly individuals afflicted with neurodegenerative diseases, especially in the context of smart homes. Within the existing literature, a multitude of datasets from both indoor and outdoor environments have surfaced, significantly contributing to the activity identification processes. One prominent dataset, the CASAS project developed by Washington State University (WSU) University, encompasses experiments conducted in indoor settings. This dataset facilitates the identification of a range of activities, such as cleaning, cooking, eating, washing hands, and even making phone calls. This article introduces a model founded on the principles of Semi-supervised Ensemble Learning, enabling the harnessing of the potential inherent in distance-based clustering analysis. This technique aids in the identification of distinct clusters, each encapsulating unique activity characteristics. These clusters serve as pivotal inputs for the subsequent classification process, which leverages supervised techniques. The outcomes of this approach exhibit great promise, as evidenced by the quality metrics' analysis, showcasing favorable results compared to the existing state-of-the-art methods. This integrated framework not only contributes to the field of HAR but also holds immense potential for enhancing the capabilities of smart homes and related applications.

Objective Assessment of Physical Activity at Home Using a Novel Floor-Vibration Monitoring System: Validation and Comparison With Wearable Activity Trackers and Indirect Calorimetry Measurements.

BACKGROUND: The self-monitoring of physical activity is an effective strategy for promoting active lifestyles. However, accurately assessing physical activity remains challenging in certain situations. This study evaluates a novel floor-vibration monitoring system to quantify housework-related physical activity. OBJECTIVE: This study aims to assess the validity of step-count and physical behavior intensity predictions of a novel floor-vibration monitoring system in comparison with the actual number of steps and indirect calorimetry measurements. The accuracy of the predictions is also compared with that of research-grade devices (ActiGraph GT9X). METHODS: The Ocha-House, located in Tokyo, serves as an independent experimental facility equipped with high-sensitivity accelerometers installed on the floor to monitor vibrations. Dedicated data processing software was developed to analyze floor-vibration signals and calculate 3 quantitative indices: floor-vibration quantity, step count, and moving distance. In total, 10 participants performed 4 different housework-related activities, wearing ActiGraph GT9X monitors on both the waist and wrist for 6 minutes each. Concurrently, floor-vibration data were collected, and the energy expenditure was measured using the Douglas bag method to determine the actual intensity of activities. RESULTS: Significant correlations (P<.001) were found between the quantity of floor vibrations, the estimated step count, the estimated moving distance, and the actual activity intensities. The step-count parameter extracted from the floor-vibration signal emerged as the most robust predictor (r2=0.82; P<.001). Multiple regression models incorporating several floor-vibration-extracted parameters showed a strong association with actual activity intensities (r2=0.88; P<.001). Both the step-count and intensity predictions made by the floor-vibration monitoring system exhibited greater accuracy than those of the ActiGraph monitor. CONCLUSIONS: Floor-vibration monitoring systems seem able to produce valid quantitative assessments of physical activity for selected housework-related activities. In the future, connected smart home systems that integrate this type of technology could be used to perform continuous and accurate evaluations of physical behaviors throughout the day.

Assessing the cognitive decline of people in the spectrum of AD by monitoring their activities of daily living in an IoT-enabled smart home environment: a cross-sectional pilot study.

Introduction: Assessing functional decline related to activities of daily living (ADLs) is deemed significant for the early diagnosis of dementia. As current assessment methods for ADLs often lack the ability to capture subtle changes, technology-based approaches are perceived as advantageous. Specifically, digital biomarkers are emerging, offering a promising avenue for research, as they allow unobtrusive and objective monitoring. Methods: A study was conducted with the involvement of 36 participants assigned to three known groups (Healthy Controls, participants with Subjective Cognitive Decline and participants with Mild Cognitive Impairment). Participants visited the CERTH-IT Smart Home, an environment that simulates a fully functional residence, and were asked to follow a protocol describing different ADL Tasks (namely Task 1 - Meal, Task 2 - Beverage and Task 3 - Snack Preparation). By utilizing data from fixed in-home sensors installed in the Smart Home, the identification of the performed Tasks and their derived features was explored through the developed CARL platform. Furthermore, differences between groups were investigated. Finally, overall feasibility and study satisfaction were evaluated. Results: The composition of the ADLs was attainable, and differentiation among the HC group compared to the SCD and the MCI groups considering the feature "Activity Duration" in Task 1 - Meal Preparation was possible, while no difference could be noted between the SCD and the MCI groups. Discussion: This ecologically valid study was determined as feasible, with participants expressing positive feedback. The findings additionally reinforce the interest and need to include people in preclinical stages of dementia in research to further evolve and develop clinically relevant digital biomarkers.

A Scheduler for Smart Home Appliances Based on a Novel Concept of Tariff Space.

The background of this work is related to the scheduling of household appliances, taking into account variations in energy costs during the day from official Brazilian domestic tariffs: constant and white. The white tariff can reach an average price of around 17% lower than the constant, but charges twice its value at peak hours. In addition to cost reduction, we propose a methodology to reduce user discomfort due to time-shifting of controllable devices, presenting a balanced solution through the analytical analysis of a new method referred to as tariff space, derived from white tariff posts. To achieve this goal, we explore the geometric properties of the movement of devices through the tariff space (geometric locus of the load), over which we can define a limited region in which the cost of a load under the white tariff will be equal to or less than the constant tariff. As a trial for the efficiency of this new methodology, we collected some benchmarks (such as execution time and memory usage) against a classic multi-objective algorithm (hierarchical) available in the language portfolio in which the project has been executed (the Julia language). As a result, while both methodologies yield similar results, the approach presented in this article demonstrates a significant reduction in processing time and memory usage, which could lead to the future implementation of the solution in a simple, low-cost embedded system like an ARM cortex M.

Smart home system using blockchain technology in green lighting environment in rural areas.

Currently, with the rapid development of smart home technology, the demand for establishing efficient and sustainable smart home systems in rural areas is increasing. However, in rural environments, the effective management and intelligent control of green energy face many challenges. To address these issues, this work aims to design a smart home system based on blockchain technology to achieve efficient energy management and intelligent control in a green lighting environment in rural areas. The main goals include improving the performance and safety of the system to meet the lighting needs of rural areas and promote sustainable development. The system comprises two primary components: the home gateway and cloud services. These components encompass functions like data monitoring and transmission, cloud storage, and remote control. The work also introduces the structural interaction, user node interaction, and the data security transmission scheme of the smart home system. Ultimately, the system's effectiveness is confirmed through simulation experiments. The results demonstrate that the system achieves the lowest latency when the transaction arrival rate is 40tps and the block size is 10. Additionally, the access control scheme based on the Hyperledger Fabric consortium chain can efficiently handle access requests for smart home resources and meet the practical application requirements within an appropriate range of security parameters. The main research conclusion is that the designed smart home system based on blockchain technology has achieved significant results in improving performance and security. This not only provides reliable lighting solutions for rural areas, but also provides important theoretical and practical guidance for the future development of smart home systems. The direction of future work includes further optimizing system performance, expanding the scope of application, and exploring more advanced blockchain technology applications in the field of smart homes. This will provide more possibilities and innovative directions for the development of future smart home systems.

Optimal control of cooling management system for energy conservation in smart home with ANNs-PSO data analytics microservice platform.

An intelligent cooling management system with a smart home application is proposed to evaluate optimal target temperatures and air conditioner fan modes, thereby maximizing energy efficiency while ensuring residents' comfort. The proposed system integrates a home energy management system with a sophisticated backend infrastructure designed to enable seamless hardware connectivity for real-time data acquisition from various sensors, a gateway, internet of things (IoT) devices, and servers. Furthermore, it serves as a platform for implementing a software data analytics model, structured upon a microservice architecture, aimed at providing optimal feedback control. The data analytics platform utilized in this research integrates two sets of artificial neural networks (ANNs) and a particle swarm optimization (PSO) algorithm for computation and control. This platform is designed not only to gather real-time ambient data and air conditioner usage records but also to regulate the air conditioner's operation autonomously. By considering aprevailing ambient air condition, the ANNs accurately predict power consumption, indoor temperature, and indoor humidity following adjustments in target temperature and fan mode. The PSO-based data analytics model efficiently selects the most suitable target temperature and fan mode, thereby achieving a dual purpose of enhancing energy conservation while minimizing potential occupant discomfort. This optimization is driven by utilizing the predicted mean vote (PMV) calculated through the analysis performed by the ANNs. Validation of the developed intelligent cooling management system was conducted in a real smart home environment inside a single detached two-story house, using an 8,000 BTU air conditioner as the testbed within an 8 × 5 m2 space accommodating four occupants. The implementation results indicate that the proposed intelligent cooling management system can reliably predict the behavior and ambient data of the air conditioner and give the best-operating settings in any different environment scenarios and therefore shows potential for energy savings in smart home applications.