Search Results (355)

Smart power grids (SGs) enhance efficiency, reliability, and sustainability by integrating distributed energy resources (DERs) such as solar panels and wind turbines. A key challenge in SGs is managing home battery charging during periods of insufficient renewable energy generation to ensure fairness, efficiency, and customer satisfaction. This paper introduces a secure reinforcement learning (RL)-based framework for optimizing battery charging coordination while addressing privacy concerns and false data injection (FDI) attacks. Privacy is preserved through Federated Learning (FL), enabling collaborative model training without sharing sensitive State of Charge (SoC) data that could reveal personal routines. To combat FDI attacks, Deep Learning (DL)-based detectors are deployed to identify malicious SoC data manipulation. To improve FL efficiency, the Change and Transmit (CAT) technique reduces communication overhead by transmitting only model parameters that experience enough change comparing to the last round. Extensive experiments validate the framework’s efficacy. The RL-based charging coordinator ensures fairness by maintaining SoC levels within thresholds and reduces overall power utilization through optimal grid power allocation. The CAT-FL approach achieves up to 93.5% communication overhead reduction, while DL-based detectors maintain high accuracy, with supervised models reaching 99.84% and anomaly detection models achieving 92.1%. Moreover, the RL agent trained via FL demonstrates strong generalization, achieving high cumulative rewards and equitable power allocation when applied to new data owners which did not participate in FL training. This framework provides a scalable, privacy-preserving, and efficient solution for energy management in SGs, offering high accuracy against FDI attacks and paving the way for the future of smart grid deployments. Full article

The increasing adoption of smart home devices has raised significant concerns regarding privacy, security, and vulnerability to cyber threats. This study addresses these challenges by presenting a federated learning framework enhanced with blockchain technology to detect intrusions in smart home environments. The proposed approach combines knowledge distillation and transfer learning to support heterogeneous IoT devices with varying computational capacities, ensuring efficient local training without compromising privacy. Blockchain technology is integrated to provide decentralized, tamper-resistant access control through Role-Based Access Control (RBAC), allowing only authenticated devices to participate in the federated learning process. This combination ensures data confidentiality, system integrity, and trust among devices. This framework’s performance was evaluated using the N-BaIoT dataset, showcasing its ability to detect anomalies caused by botnets such as Mirai and BASHLITE across diverse IoT devices. Results demonstrate significant improvements in intrusion detection accuracy, particularly for resource-constrained devices, while maintaining privacy and adaptability in dynamic smart home environments. These findings highlight the potential of this blockchain-enhanced federated learning system to offer a scalable, robust, and privacy-preserving solution for securing smart homes against evolving threats. Full article

Gesture recognition technology has emerged as a transformative solution for natural and intuitive human–computer interaction (HCI), offering touch-free operation across diverse fields such as healthcare, gaming, and smart home systems. In mobile contexts, where hygiene, convenience, and the ability to operate under resource constraints are critical, hand gesture recognition provides a compelling alternative to traditional touch-based interfaces. However, implementing effective gesture recognition in real-world mobile settings involves challenges such as limited computational power, varying environmental conditions, and the requirement for robust offline–online data management. In this study, we introduce ThumbsUp, which is a gesture-driven system, and employ a partially systematic literature review approach (inspired by core PRISMA guidelines) to identify the key research gaps in mobile gesture recognition. By incorporating insights from deep learning–based methods (e.g., CNNs and Transformers) while focusing on low resource consumption, we leverage Google’s MediaPipe in our framework for real-time detection of 21 hand landmarks and adaptive lighting pre-processing, enabling accurate recognition of a “thumbs-up” gesture. The system features a secure queue-based offline–cloud synchronization model, which ensures that the captured images and metadata (encrypted with AES-GCM) remain consistent and accessible even with intermittent connectivity. Experimental results under dynamic lighting, distance variations, and partially cluttered environments confirm the system’s superior low-light performance and decreased resource consumption compared to baseline camera applications. Additionally, we highlight the feasibility of extending ThumbsUp to incorporate AI-driven enhancements for abrupt lighting changes and, in the future, electromyographic (EMG) signals for users with motor impairments. Our comprehensive evaluation demonstrates that ThumbsUp maintains robust performance on typical mobile hardware, showing resilience to unstable network conditions and minimal reliance on high-end GPUs. These findings offer new perspectives for deploying gesture-based interfaces in the broader IoT ecosystem, thus paving the way toward secure, efficient, and inclusive mobile HCI solutions. Full article

Intelligent safety and security is significant for preventing risks, ensuring information security and promoting sustainable social development, making it an indispensable part of modern society. Current research primarily focuses on the knowledge base and research hotspots in the field of intelligent safety and security. However, a comprehensive mapping of its overall knowledge structure remains lacking. A total of 1400 publications from the Web of Science Core Collection (2013–2023) are analyzed using VOSviewer and CiteSpace, through which co-occurrence analysis, keyword burst detection, and co-citation analysis are conducted. Through this approach, this analysis systematically uncovers the core themes, evolutionary trajectories, and emerging trends in intelligent safety and security research. Unlike previous bibliometric studies, this study is the first to integrate multiple visualization techniques to construct a holistic framework of the intelligent safety and security knowledge system. Additionally, it offers an in-depth analysis of key topics such as IoT security, intelligent transportation systems, smart cities, and smart grids, providing quantitative insights to guide future research directions. The results show that the most significant number of publications are from China; the top position on the list of papers published by related institutions is occupied by King Saud University from Saudi Arabia. Renewable and Sustainable Energy Reviews, Sustainable Cities and Society, and IEEE Transactions on Intelligent Transportation Systems are identified as the leading publications in this field. The decentralization of blockchain technology, the security and challenges of the Internet of Things (IoT), and research on intelligent cities and smart homes have formed the knowledge base for innovative security research. The four key directions of intelligent safety and security research mainly comprise IoT security, intelligent transportation systems, traffic safety and its far-reaching impact, and the utilization of smart grids and renewable energy. Research on IoT technology, security, and limitations is at the forefront of interest in this area. Full article

Every year, more and more electronic devices are used in households, which certainly leads to an increase in the total number of communications between devices. During communication, a huge amount of information is transmitted, which can be critical or even malicious. To avoid the transmission of unnecessary information, a filtering mechanism can be applied. Filtering is a long-standing method used by network engineers to segregate and thus block unwanted traffic from reaching certain devices. In this work, we show how to apply this to the Internet of Things (IoT) Smart Home domain as it introduces numerous networked devices into our daily lives. To analyse the positive influence of filtering on security and privacy, we offer the results from our in-depth STRIDE and LINDDUN analysis of several Smart Home scenarios before and after the application. To show that filtering can be applied to other IoT domains, we offer a brief glimpse into the domain of smart cars. Full article

The Internet of Things (IoT) has witnessed significant growth in recent years, with Bluetooth Low Energy (BLE) emerging as a key enabler of low-power, low-cost wireless connectivity. This review article provides an overview of the evolution of Bluetooth technology, focusing on the role of BLE in the IoT ecosystem. It examines the current state of BLE, including its applications, challenges, limitations, and recent advancements in areas such as security, power management, and mesh networking. The recent release of Bluetooth Low Energy version 6.0 by the Bluetooth Special Interest Group (SIG) highlights the technology’s ongoing evolution and growing importance within the IoT. However, this rapid development highlights a gap in the current literature, a lack of comprehensive, up-to-date reviews that fully capture the contemporary landscape of BLE in IoT applications. This paper analyzes the emerging trends and future directions for BLE, including the integration of artificial intelligence, machine learning, and audio capabilities. The analysis also considers the alignment of BLE features with the United Nations’ Sustainable Development Goals (SDGs), particularly energy efficiency, sustainable cities, and climate action. By examining the development and deployment of BLE technology, this article aims to provide insights into the opportunities and challenges associated with its adoption in various IoT applications, from smart homes and cities to industrial automation and healthcare. This review highlights the significance of the evolution of BLE in shaping the future of wireless communication and IoT, and provides a foundation for further research and innovation in this field. Full article

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. Full article

Data-driven technologies in smart buildings offer significant opportunities to enhance energy efficiency, sustainability, and occupant comfort. However, the existing literature often lacks a holistic examination of the technological advancements, adoption barriers, and business models necessary to realize these benefits. To address this gap, this scoping review synthesizes current research on these technologies, identifies factors influencing their adoption, and examines supporting business models. Inspired by the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines, a structured search of the literature across four major databases yielded 112 relevant studies. The key technologies identified included big data analytics, Artificial Intelligence, Machine Learning, the Internet of Things, Wireless Sensor Networks, Edge and Cloud Computing, Blockchain, Digital Twins, and Geographic Information Systems. Energy optimization is further achieved through integrating renewable energy resources and advanced energy management systems, such as Home Energy Management Systems and Building Energy Management Systems. Factors influencing adoption are categorized into social influences, individual perceptions, cost considerations, security and privacy concerns, and data quality issues. The analysis of business models emphasizes the need to align technological innovations with market needs, focusing on value propositions like cost savings and efficiency improvements. Despite the benefits, challenges such as high initial costs, technical complexities, security risks, and user acceptance hinder their widespread adoption. This review highlights the importance of addressing these challenges through the development of cost-effective, interoperable, secure, and user-centric solutions, offering a roadmap for future research and industry applications. Full article

In the current era of advanced IoT technology, human occupancy monitoring and positioning technology is widely used in various scenarios. For example, it can optimize passenger flow in public transportation systems, enhance safety in large shopping malls, and adjust smart home devices based on the location and number of occupants for energy savings. Additionally, in homes requiring special care, it can provide timely assistance. However, this technology faces limitations such as privacy concerns, environmental factors, and costs. Traditional cameras may not effectively address these issues, but infrared thermal sensors can offer similar applications while overcoming these challenges. Infrared thermal sensors detect the infrared heat emitted by the human body, protecting privacy and functioning effectively day and night with low power consumption, making them ideal for continuous monitoring scenarios like security systems or elderly care. In this study, we propose a system using the AMG8833, an 8 × 8 Infrared Thermal Array Sensor. The sensor data are processed through interpolation, adaptive thresholding, and blob detection, and the merged human heat signatures are separated. To enhance stability in human position estimation, a dynamic sliding window adjusts its size based on movement speed, effectively handling environmental changes and uncertainties. Full article

In the context of the increasing integration of renewable energy sources (RES) and smart devices in domestic applications, the implementation of Home Energy Management Systems (HEMS) is becoming a pivotal factor in optimizing energy usage and reducing costs. This review examines the role of reinforcement learning (RL) in the advancement of HEMS, presenting it as a powerful tool for the adaptive management of complex, real-time energy demands. This review is notable for its comprehensive examination of the applications of RL-based methods and tools in HEMS, which encompasses demand response, load scheduling, and renewable energy integration. Furthermore, the integration of RL within distributed automation and Internet of Things (IoT) frameworks is emphasized in the review as a means of facilitating autonomous, data-driven control. Despite the considerable potential of this approach, the authors identify a number of challenges that require further investigation, including the need for robust data security and scalable solutions. It is recommended that future research place greater emphasis on real applications and case studies, with the objective of bridging the gap between theoretical models and practical implementations. The objective is to achieve resilient and secure energy management in residential and prosumer buildings, particularly within local microgrids. Full article

Even while living circumstances and construction techniques have generally improved, occupants of these spaces frequently feel unsatisfied with the sense of security they provide, which leads to looking for and eventually enacting ever-more-effective safety precautions. The continuous uncertainty that contemporary individuals experience, particularly with regard to their protection in places like cities, prompted the field of computing to design smart devices that attempt to reduce threats and ultimately strengthen people’s sense of protection. Intelligent apps were developed to provide protection and make a residence a smart and safe home. The proliferation of technology for smart homes necessitates the implementation of rigorous safety precautions to protect users’ personal information and avoid illegal access. The importance of establishing cyber security has been recognized by academic and business institutions all around the globe. Providing reliable computation for the Internet of Things (IoT) is also crucial. A new method for enhancing safety in smart home environments’ sustainability using IoT devices is presented in this paper, combining the Whale Optimization Algorithm (WOA) with Deep Convolutional Neural Networks (DCNNs). WOA-DCNN hybridization seeks to enhance safety measures by efficiently identifying and averting possible attacks in real time. We show how effective the proposed approach is in defending smart home systems from a range of safety risks via in-depth testing and analysis. By providing a potential path for protecting smart home surroundings in a world that is growing more linked, this research advances the state of the art in IoT security. Full article

Internet of Things (IoT) devices are ubiquitous in various applications, such as smart homes, asset and people tracking, and city management systems. However, their deployment in adverse conditions, including unstable internet connectivity and power sources, present new cybersecurity challenges through new attack vectors. The LoRaWAN protocol, with its open and distributed network architecture, has gained prominence as a leading LPWAN solution, presenting novel security challenges. This paper proposes the implementation of machine learning algorithms, specifically the K-Nearest Neighbours (KNN) algorithm, within an Intrusion Detection System (IDS) for LoRaWAN networks. Through behavioural analysis based on previously observed packet patterns, the system can detect potential intrusions that may disrupt critical tracking services. Initial simulated packet classification attained over 90% accuracy. By integrating the Suricata IDS and extending it through a custom toolset, sophisticated rule sets are incorporated to generate confidence metrics to classify packets as either presenting an abnormal or normal behaviour. The current work uses third-party multi-vendor sensor data obtained in the city of Lisbon for training and validating the models. The results show the efficacy of the proposed technique in evaluating received packets, logging relevant parameters in the database, and accurately identifying intrusions or expected device behaviours. We considered two use cases for evaluating our work: one with a more traditional approach where the devices and network are static, and another where we assume that both the devices and the network are mobile; for example, when we need to report data back from sensors on a rail infrastructure to a mobile LoRaWAN gateway onboard a train. Full article

Current research in home automation focuses on integrating emerging technologies like Internet of Things (IoT) and machine learning to create smart home solutions that offer enhanced convenience, efficiency, and security. Benefits include remote control of household devices, optimized energy usage through automated systems, and improved user experience with real-time monitoring and alerts. In this study, a TinyML (Tiny Machine Learning)-based keyword spotting machine learning model and system is proposed which enables voice-based home automation. The proposed system allows users to control household devices through voice commands with minimal computational resources and real-time performance. The main objective of this research is to develop the TinyML model for resource-constrained devices. The system enables home systems to efficiently recognize specific keywords or phrases by integrating voice control for enhanced user convenience and accessibility. In this research, the different voice keywords of users of different age groups have been collected in the home environment and trained using machine learning algorithms. An IoT-based system is then developed utilizing the TinyML model to recognize a specific voice command and perform home automation tasks. The model has achieved 98% accuracy with an F1 score of 1.00 and 92% recall. The quantized model uses Latency of 5 ms, 7.9 K of RAM and 43.7 K of flash for keyword classification, which is the best fit for any resource-constrained devices. The proposed system demonstrates the viability of deploying a keyword spotting model for home automation on resource-constrained IoT devices. The research helps in building efficient and user-friendly smart home solutions, enhancing the accessibility and functionality of home automation systems. Full article

Reinforcement learning (RL) is proven effective in optimizing home battery charging coordination within smart grids. However, its vulnerability to adversarial behavior poses a significant challenge to the security and fairness of the charging process. In this study, we, first, craft five stealthy false data injection (FDI) attacks that under-report the state-of-charge (SoC) values to deceive the RL agent into prioritizing their charging requests, and then, we investigate the impact of these attacks on the charging coordination system. Our evaluations demonstrate that attackers can increase their chances of charging compared to honest consumers. As a result, honest consumers experience reduced charging levels for their batteries, leading to a degradation in the system’s performance in terms of fairness, consumer satisfaction, and overall reward. These negative effects become more severe as the amount of power allocated for charging decreases and as the number of attackers in the system increases. Since the total available power for charging is limited, some honest consumers with genuinely low SoC values are not selected, creating a significant disparity in battery charging levels between honest and malicious consumers. To counter this serious threat, we develop a deep learning-based FDI attack detector and evaluated it using a real-world dataset. Our experiments show that our detector can identify malicious consumers with high accuracy and low false alarm rates, effectively protecting the RL-based charging coordination system from FDI attacks and mitigating the negative impacts of these attacks. Full article

The number of smart homes is increasing steadily. One of the first technologies that comes to mind when talking about smart homes is Zigbee, which stands out for its low cost, low latency, low power consumption, and mesh networking capabilities. One of the key features of Zigbee is the encryption of payloads within its frames for security purposes. However, being able to decrypt this payload is crucial for fully understanding its operation and for purposes such as testing the network’s security. Therefore, in this paper, we present the decrypted Zigbee IoT Network Traffic dataset, ZigBeeNet. We captured packets using Wireshark in real time from a smart home with 15 Zigbee devices over 20 days and saved them in pcap files. Additionally, we used a key extraction method to obtain the network key, decrypt the payload data, and analyze the characteristic features of network traffic, which we present in this paper. ZigBeeNet will be useful in wider areas than existing datasets with its ability to support network security research, pattern analysis, network performance analysis, and Zigbee traffic generator. We believe that this open-source dataset will contribute significantly to a wide range of industrial and academic research applications. Full article