Internet of Things最新文献

{"title":"Zero-knowledge machine learning models for blockchain peer-to-peer energy trading","authors":"Caixiang Fan ,&nbsp;Amirhossein Sohrabbeig ,&nbsp;Petr Musilek","doi":"10.1016/j.iot.2025.101638","DOIUrl":"10.1016/j.iot.2025.101638","url":null,"abstract":"<div><div>Blockchain-based peer-to-peer energy trading enables individuals to directly share renewable energy using Internet of Things technologies. However, it faces significant challenges related to privacy, scalability, and the integration of advanced artificial intelligence. To address these issues, this article proposes zkPET, a secure and intelligent peer-to-peer energy trading framework. zkPET integrates machine learning and blockchain with advanced cryptographic techniques of zero-knowledge machine learning to protect user data while enabling intelligent decision making. In the zkPET framework, the computationally intensive operations of various machine learning models are executed off-chain, and only succinct cryptographic proofs of these computations are uploaded to the blockchain for verification and recording. In addition, a time-series clustering approach is incorporated into federated learning to enhance both inference accuracy and the efficiency of proof generation. Experimental validation using the zero-knowledge proof tool EZKL and a real-world electricity dataset demonstrates the feasibility and effectiveness of zkPET. The results underscore its potential to significantly improve privacy, scalability, and computational efficiency in decentralized energy trading, contributing to the advancement of secure and intelligent energy markets.</div></div>","PeriodicalId":29968,"journal":{"name":"Internet of Things","volume":"32 ","pages":"Article 101638"},"PeriodicalIF":6.0,"publicationDate":"2025-05-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144169727","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A lightweight hybrid approach for intrusion detection systems using a chi-square feature selection approach in IoT","authors":"Hafsa Benaddi ,&nbsp;Mohammed Jouhari ,&nbsp;Omar Elharrouss","doi":"10.1016/j.iot.2025.101624","DOIUrl":"10.1016/j.iot.2025.101624","url":null,"abstract":"<div><div>Protecting IoT networks from cyber threats is challenging, especially with resource-constrained devices. This paper proposes an efficient, lightweight hybrid intrusion detection system (IDS) specifically optimized for IoT devices. Our innovative approach integrates convolutional neural networks (CNN) for effective spatial feature extraction and bidirectional long short-term memory (BiLSTM) networks for capturing temporal dependencies. Crucially, we employ a chi-square (<span><math><msup><mrow><mi>χ</mi></mrow><mrow><mn>2</mn></mrow></msup></math></span>) feature selection method, significantly reducing input complexity by selecting the 20 most relevant features from the UNSW-NB15 dataset. Benchmarking against recent IDS methods, our model achieved outstanding accuracy: 97.90% for binary classification and 97.09% for multiclass scenarios, clearly outperforming existing approaches. Additionally, computational performance evaluation reveals rapid prediction times (1.1 s binary; 2.10 s multiclass), demonstrating suitability for real-time IoT deployment. This study illustrates a balanced trade-off between high accuracy and low computational demand, highlighting the practical benefits of advanced, resource-efficient IDS solutions for IoT security.</div></div>","PeriodicalId":29968,"journal":{"name":"Internet of Things","volume":"32 ","pages":"Article 101624"},"PeriodicalIF":6.0,"publicationDate":"2025-05-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144169776","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Enhancing medical digital twins within metaverse using blockchain, NFTs and LLMs","authors":"Ruba Islayem ,&nbsp;Ahmad Musamih ,&nbsp;Khaled Salah ,&nbsp;Raja Jayaraman ,&nbsp;Ibrar Yaqoob","doi":"10.1016/j.iot.2025.101648","DOIUrl":"10.1016/j.iot.2025.101648","url":null,"abstract":"<div><div>Medical digital twins (MDTs) are rapidly emerging as transformative tools in healthcare. They provide virtual representations of medical devices and systems that facilitate real-time analysis and enhance decision-making. However, challenges such as secure data management, access control, and the lack of immersive and intelligent patient interactions limit their effectiveness. In this paper, we propose a solution integrating blockchain technology, Non-Fungible Tokens (NFTs), and Large Language Models (LLMs) within a metaverse environment to enhance MDT functionality. Blockchain and NFTs ensure secure ownership and access control, while the metaverse offers an engaging platform for user interaction. An LLM-powered non-player character (NPC) enables intelligent real-time user interactions and personalized insights. We develop two blockchain smart contracts for user registration, NFT ownership, and access control, and utilize decentralized InterPlanetary File System (IPFS) storage for the metaverse, MDT metadata, and interaction logs. We present the system architecture, sequence diagrams, and algorithms, along with the implementation and testing details. We conduct cost, security, and response time analyses to evaluate the smart contracts and LLM performance and compare our solution with existing approaches. We discuss practical implications, as well as challenges and limitations of the proposed solution. Finally, we explore the generalization of our system for various applications. The smart contract code and metaverse files are publicly available on GitHub.</div></div>","PeriodicalId":29968,"journal":{"name":"Internet of Things","volume":"32 ","pages":"Article 101648"},"PeriodicalIF":6.0,"publicationDate":"2025-05-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144154536","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"An adaptive backoff algorithm for enhanced MAC-layer security and operational efficiency in IoT and cyber–physical systems","authors":"Sofiane Hamrioui ,&nbsp;Redouane Djelouah ,&nbsp;Pascal Lorenz","doi":"10.1016/j.iot.2025.101641","DOIUrl":"10.1016/j.iot.2025.101641","url":null,"abstract":"<div><div>The proliferation of industrial IoT and cyber–physical systems demands Medium Access Control (MAC) protocols that simultaneously address security threats and operational efficiency in resource-constrained environments. Existing solutions frequently fail to provide adequate protection against real-time threats like jamming and denial-of-service (DoS) attacks while maintaining performance. We present the Adaptive MAC-layer Backoff Algorithm (AMBA), a novel protocol that enhances security, efficiency, and resilience through dynamic backoff adaptation based on real-time traffic analysis and physical-layer feedback. AMBA achieves: (1) 20 Mbps peak throughput (15.5 Mbps under jamming; 17 Mbps under DoS), (2) 50% lower latency than JR-MAC, (3) 75% improvement in packet loss resilience, and (4) 20%–30% higher Security Threat Resilience Metric (STRM) scores against diverse attacks. Evaluations demonstrate AMBA’s superiority over existing protocols while meeting the stringent reliability requirements of industrial IoT and vehicular networks. The solution’s lightweight design and scalability make it particularly suitable for next-generation cyber–physical systems where security and performance must coexist.</div></div>","PeriodicalId":29968,"journal":{"name":"Internet of Things","volume":"32 ","pages":"Article 101641"},"PeriodicalIF":6.0,"publicationDate":"2025-05-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144137284","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Optimising secure and sustainable smart home configurations","authors":"Daniel Muñoz-Heredia,&nbsp;Ángel Jesús Varela-Vaca,&nbsp;Diana Borrego,&nbsp;María Teresa Gómez-López","doi":"10.1016/j.iot.2025.101637","DOIUrl":"10.1016/j.iot.2025.101637","url":null,"abstract":"<div><div>As the adoption of smart devices accelerates rapidly in smart homes worldwide, the variety of available devices on the market is also diversifying. This creates a challenge for users, who must choose devices that best meet their needs, and for system designers, who must ensure these devices integrate efficiently within a connected ecosystem.</div><div>In response to this challenge, the solution presented in this work provides a metamodel that gathers the smart home features, including attributes related to security, usability, connectivity and sustainability. These features are used to create personalised configurations of smart homes that meet user requirements. This is achieved through the creation of multi-objective optimisation problems focused on improving: security, to ensure network and personal data protection; usability, to facilitate the easy management of the environment; connectivity, to maintain seamless interaction between both existing and future devices; and sustainability, which assesses the environmental impact and energy efficiency of the technological ecosystem. The implementation of the proposal is available and a set of experiments have been developed to evaluate the proposal’s applicability using real devices, being reproducible and replicable.</div></div>","PeriodicalId":29968,"journal":{"name":"Internet of Things","volume":"32 ","pages":"Article 101637"},"PeriodicalIF":6.0,"publicationDate":"2025-05-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144123259","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"An energy-focused model for batteryless IoT: Vortex wireless power transfer and fog computing in 6 G networks","authors":"Mehdi Hosseinzadeh ,&nbsp;Jawad Tanveer ,&nbsp;Saqib Ali ,&nbsp;Marcia L. Baptista ,&nbsp;Farhad Soleimanian Gharehchopogh ,&nbsp;Shakia Rajabi ,&nbsp;Thantrira Porntaveetus ,&nbsp;Sang-Woong Lee","doi":"10.1016/j.iot.2025.101657","DOIUrl":"10.1016/j.iot.2025.101657","url":null,"abstract":"<div><div>The Internet of Things (IoT) refers to the networked interconnection of devices that collect, exchange, and analyze data to enable intelligent applications. In emerging sixth-generation (6 G) networks, batteryless IoT devices have gained significant attention, as they rely on ambient energy harvesting rather than traditional batteries. This paper presents an energy-focused model for a 6G-enabled batteryless IoT network that integrates Vortex Wireless Power Transfer (WPT) with fog node coordination to manage energy harvesting and computation offloading. WPT exploits electromagnetic resonance to deliver energy wirelessly. Our vortex‐based model applies exponential attenuation, enhancing energy harvesting for batteryless IoT devices. Then system dynamically assigns IoT devices to optimal WPT zones based on coverage and received power, while simultaneously determining whether tasks should be executed locally or offloaded to Mobile Edge Computing (MEC)-enabled fog nodes, based on real-time energy and latency constraints. To solve the result of the NP-hard optimization problem, we develop an Enhanced Adaptive Quantum Binary Particle Swarm Optimization (EAQBPSO) algorithm that effectively balances workload distribution, energy harvesting, and consumption. Simulation results indicate that our approach significantly outperform traditional methods, achieving improvements of up to 71 % in energy efficiency, nearly 87 % in energy harvesting efficiency, and reducing average energy consumption per task by over 40 %.</div></div>","PeriodicalId":29968,"journal":{"name":"Internet of Things","volume":"32 ","pages":"Article 101657"},"PeriodicalIF":6.0,"publicationDate":"2025-05-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144134975","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Digital Twin-driven federated learning and reinforcement learning-based offloading for energy-efficient distributed intelligence in IoT networks","authors":"Klea Elmazi ,&nbsp;Donald Elmazi ,&nbsp;Jonatan Lerga","doi":"10.1016/j.iot.2025.101640","DOIUrl":"10.1016/j.iot.2025.101640","url":null,"abstract":"<div><div>Improved frameworks for delivering both intelligence and effectiveness under strict constraints on resources are required due to the Internet of Things’ (IoT) devices’ rapid expansion and the resulting increase in sensor-generated data. In response, this research considers a joint learning-offloading optimization approach and presents an improved framework for energy-efficient distributed intelligence in sensor networks. Our method dynamically allocates computational tasks across resource-constrained sensors and more powerful edge servers through incorporating Federated Learning (FL) with adaptive offloading techniques. This allows collaborative model training across IoT devices. We suggest a multi-objective optimization problem that simultaneously maximizes learning accuracy and convergence time and minimizes energy usage with the objective to solve the dual issues of energy consumption and model performance. To create energy-efficient distributed intelligence in IoT sensor networks, our suggested framework combines FL, Digital Twin (DT), and sophisticated Reinforcement Learning (RL)-based decision-making engine. In order to predict short-term system dynamics, the DT uses linear regression and moving averages for predictive analytics based on real-time data from sensor nodes, such as battery levels, CPU loads, and network latencies. A Dueling Double Deep Q-Network (D3QN) agent with Prioritized Experience Replay (PER) and multi-step returns is directed by these predictions and dynamically chooses between offloading and local processing depending on the operating environment. According to experimental data, our method effectively keeps final battery levels over 85% while allowing the offloading to reduce local CPU drain. We compare the proposed framework with two baseline methods. The evaluation results show that the pure local strategy obtains a slightly increased average battery level, about 91%, but never offloads tasks, the naïve offload method maintains a lower average battery level, about 70%, than our RL agent’s converged policy, about 85%.</div></div>","PeriodicalId":29968,"journal":{"name":"Internet of Things","volume":"32 ","pages":"Article 101640"},"PeriodicalIF":6.0,"publicationDate":"2025-05-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144123151","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"3D Printed microstrip antenna for symbiotic communication: WiFi backscatter and bit rate evaluation for IoT","authors":"Muhammed Yusuf Onay ,&nbsp;Burak Dokmetas","doi":"10.1016/j.iot.2025.101643","DOIUrl":"10.1016/j.iot.2025.101643","url":null,"abstract":"<div><div>This work presents the design, experimental validation of a novel 3D-printed microstrip antenna operating at 2.4 GHz for WiFi backscatter communication in IoT applications and its performance evaluation on the communication protocol proposed in the work. The antenna is manufactured using PREPERM ABS material, which is specifically designed for high-frequency RF applications. It meets the requirements of 5G systems by ensuring high efficiency and low power loss in transmission. The antenna, integrated into the symbiotic/interference communication system, realizes low-power data transmission by utilizing existing WiFi signals. The signal power levels of each antenna in the system are tested with experimental measurements performed in a real-world environment. Then, the obtained data is used to calculate the total bit transmission rate of the system for two different scenarios proposed in the communication protocol. The proposed antenna achieves 80% efficiency, offering 10%–15% higher performance than conventional RFID-based designs, with a 5 dB gain improvement. Additionally, theoretical analysis reveals that the bit transmission rate is approximately 1.5 bps/Hz higher than experimental results, demonstrating the impact of real-world constraints on system performance. The results provide a comparative analysis of the relationship between experimental and analytical approaches in optimizing the total bit transmission rate of WiFi backscatter communication systems under different benchmarks. These findings confirm the antenna’s efficiency and enhanced performance for energy-efficient IoT applications. This research clearly demonstrates the potential of customized 3D-printed antennas and their applicability in backscatter systems to advance next-generation communication technologies.</div></div>","PeriodicalId":29968,"journal":{"name":"Internet of Things","volume":"32 ","pages":"Article 101643"},"PeriodicalIF":6.0,"publicationDate":"2025-05-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144098895","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Smart-contracts-driven personal carbon credit management in smart cities: A review and future research directions","authors":"Reem S. Alharbi ,&nbsp;Farookh Khadeer Hussain","doi":"10.1016/j.iot.2025.101636","DOIUrl":"10.1016/j.iot.2025.101636","url":null,"abstract":"<div><div>This paper examines personal carbon credits from energy saving in smart cities. Personal carbon credits are carbon credits owned by individuals who reduce their household greenhouse gas emissions by a real and verifiable value. This paper presents a systematic literature review (SLR) to examine how individuals can generate carbon credits as a result of energy saving measures in smart cities. The SLR includes research, reviews and conference papers from 2013–2024 from the IEEE, Springer, ACM and ScienceDirect databases. A total of 14 articles were selected for this SLR based on the titles, keywords and abstracts by following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. We divide the studies into two groups. The first group pertains to blockchain and the Internet of Things (IoT) for carbon trading while the other group pertains to building energy for carbon credits. A comparative analysis was undertaken to understand how individuals can generate carbon credits by reducing their energy consumption. The results of the SLR show there is a lack of studies on how individuals can obtain carbon credits based on their energy consumption behavior in smart cities. Most studies are concerned about carbon emissions trading and how to reduce carbon dioxide emissions in the building sector. Finally, this paper highlights the crucial need for future research on personal carbon credits systems to enhance their scalability, effectiveness, and efficiency.</div></div>","PeriodicalId":29968,"journal":{"name":"Internet of Things","volume":"32 ","pages":"Article 101636"},"PeriodicalIF":6.0,"publicationDate":"2025-05-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144134976","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Dynamic spectrum sharing in heterogeneous wireless networks using deep reinforcement learning","authors":"Sulaimon Oyeniyi Adebayo ,&nbsp;Abdulaziz Barnawi ,&nbsp;Tarek Sheltami ,&nbsp;Muhammad Felemban","doi":"10.1016/j.iot.2025.101635","DOIUrl":"10.1016/j.iot.2025.101635","url":null,"abstract":"<div><div>The rapid expansion of wireless networks demands efficient spectrum allocation. Dynamic Spectrum Sharing (DSS) is a technology that allows multiple wireless networks to share the same frequency spectrum dynamically. It is an effective technique for optimizing spectrum use, particularly in heterogeneous environments where multiple wireless technologies with diverse spectrum access requirements coexist, often leading to interference challenges and increased spectrum competition. This research proposes an enhanced DSS technique based on Deep Reinforcement Learning (DRL). The proposed method enables an effective sharing of the available spectrum between two access technologies, namely Long Term Evolution (LTE) and Narrowband IoT (NB-IoT). The study optimizes throughput through DRL methods, including Deep Q-Networks (DQN), conducting experiments in three phases: LTE-DRL coexistence, NB-IoT-DRL coexistence, and LTE-NB-IoT coexistence. Results show that deep learning enhances the LTE-DRL system’s convergence rate and throughput, achieving over 85% throughput with convergence times as low as 24 milliseconds (ms). The study highlights the trade-offs between parameters such as probabilities (arrival, successful transmission, and retransmission), packet expiry duration, learning rate, discount factor, fairness index, and the neural network architecture as well as the parameters’ impact on the overall system throughput. NB-IoT coexistence with DRL shows similar results with a slight decrement in throughput and negligibly longer convergence rate, while the coexistence of LTE and NB-IoT results in throughput of around 70% for each of the LTE and NB-IoT systems due to increased spectrum competition and increased complexity of the operating environment. This work offers insights into optimizing spectrum sharing using DRL and underscores the balance between various parameters for efficient spectrum management.</div></div>","PeriodicalId":29968,"journal":{"name":"Internet of Things","volume":"32 ","pages":"Article 101635"},"PeriodicalIF":6.0,"publicationDate":"2025-05-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144107893","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}