Internet of Things最新文献_第3页

IoV-TwinChain: Predictive maintenance of vehicles in internet of vehicles through digital twin and blockchain

IF 6 3区计算机科学

Internet of Things Pub Date : 2025-01-20 DOI: 10.1016/j.iot.2025.101514

Mubashar Iqbal , Sabah Suhail , Raimundas Matulevičius , Faiz Ali Shah , Saif Ur Rehman Malik , Kieran McLaughlin

{"title":"IoV-TwinChain: Predictive maintenance of vehicles in internet of vehicles through digital twin and blockchain","authors":"Mubashar Iqbal , Sabah Suhail , Raimundas Matulevičius , Faiz Ali Shah , Saif Ur Rehman Malik , Kieran McLaughlin","doi":"10.1016/j.iot.2025.101514","DOIUrl":"10.1016/j.iot.2025.101514","url":null,"abstract":"<div><div>Vehicular networks are experiencing a significant transformation driven by integrating connected vehicles and Intelligent Transportation Systems (ITS). The Internet of Vehicles (IoV) is a rapidly evolving domain within ITS that connects vehicles, infrastructure, and smart devices to facilitate seamless data exchange. This data encompasses vital information, and by leveraging it, vehicles can make informed decisions and adapt to real-time situations. For instance, traditional vehicle maintenance practices often rely on reactive approaches, addressing issues after failures occur, which can lead to safety risks, costly repairs, and disruptions. Thus, there is a pressing need for proactive solutions to identify vehicle failures before they escalate. Accordingly, we propose the IoV-TwinChain framework integrating Digital Twin (DT), Machine Learning (ML), and blockchain for performing predictive maintenance of vehicles in the IoV by monitoring vehicle operating conditions to prevent road breakdowns and failures. DT provides real-time monitoring of vehicle operating conditions, while ML facilitates data-driven predictions for the predictive maintenance of vehicles. The IoV-TwinChain framework utilizes blockchain for data integrity and traceability within physical and twin environments. We implement a Proof of Concept (PoC) of the IoV-TwinChain framework using Microsoft Azure DT, ML models such as Random Forest and XGBoost, and Ethereum blockchain. Additionally, we formally verify the IoV-TwinChain framework correctness using High-Level Petri Nets and Bounded Model-Checking methods. With our PoC implementation and formal verification, we demonstrate that the IoV-TwinChain framework effectively enhances the predictive maintenance capabilities of vehicles in the IoV and ensures the reliability and accuracy of the system.</div></div>","PeriodicalId":29968,"journal":{"name":"Internet of Things","volume":"30 ","pages":"Article 101514"},"PeriodicalIF":6.0,"publicationDate":"2025-01-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143223139","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}

引用次数: 0

Supporting constrained devices and networks in the decentralised Internet of Things

IF 6 3区计算机科学

Internet of Things Pub Date : 2025-01-19 DOI: 10.1016/j.iot.2025.101496

Ryan Kurte, Zoran Salcic, Kevin I-Kai Wang

{"title":"Supporting constrained devices and networks in the decentralised Internet of Things","authors":"Ryan Kurte, Zoran Salcic, Kevin I-Kai Wang","doi":"10.1016/j.iot.2025.101496","DOIUrl":"10.1016/j.iot.2025.101496","url":null,"abstract":"<div><div>The ongoing growth of the Internet of Things (IoT) offers great opportunities to improve the way we understand and interact with the physical world. However, existing approaches face a number of challenges from providing scalable and reliable services to users to addressing the need for security and privacy. We believe decentralised architectures offer a valuable alternative to centralised approaches. However, increased performance and communication requirements render existing decentralised technologies unsuitable for use with the constrained edge- and end-devices popular in the IoT. In this paper we address these limitations. First by extending DSF-IoT with a novel mechanism for delegation to enable transparent interactions with and between IoT services on constrained and unconstrained end-devices without the trust and privacy boundaries of existing approaches. Second, delegation is demonstrated through the creation of what we believe to be the first real-world heterogeneous decentralised IoT network spanning WSAN connected microcontrollers, Arm-based single board computers, and unconstrained x64 devices. Third, we qualify DSF-IoT for use on constrained edge-devices through the development of a constrained gateway prototype and test-bench for measuring the performance of IoT brokers under varying operating conditions. This demonstrates that DSF-IoT offers equivalent (or improved) performance to popular CoAP and MQTT brokers on constrained edge-devices while providing historical data storage and access as well as end-to-end trust and privacy. Finally we perform a functional comparison of DSF-IoT against existing protocols and research to highlight the advantages of our approach and the suitability of DSF-IoTs and delegation for use with constrained edge- and end-devices to enable the creation of a truly heterogeneous future decentralised Internet of Things.</div></div>","PeriodicalId":29968,"journal":{"name":"Internet of Things","volume":"30 ","pages":"Article 101496"},"PeriodicalIF":6.0,"publicationDate":"2025-01-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143223133","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Secure and Efficient Lightweight Authentication Protocol (SELAP) for multi-sector IoT applications

IF 6 3区计算机科学

Internet of Things Pub Date : 2025-01-17 DOI: 10.1016/j.iot.2025.101499

Alireza Javadi , Sadegh Sadeghi , Peyman Pahlevani , Nasour Bagheri , Samad Rostampour , Ygal Bendavid

{"title":"Secure and Efficient Lightweight Authentication Protocol (SELAP) for multi-sector IoT applications","authors":"Alireza Javadi , Sadegh Sadeghi , Peyman Pahlevani , Nasour Bagheri , Samad Rostampour , Ygal Bendavid","doi":"10.1016/j.iot.2025.101499","DOIUrl":"10.1016/j.iot.2025.101499","url":null,"abstract":"<div><div>The growing use of Internet of Things (IoT) systems in daily life highlights the critical need for robust IoT security. Recent efforts have focused on developing methods to improve security, leading to many published studies. IoT systems face challenges beyond security, including energy efficiency and communication bandwidth, making security design complex. This article reviews recent authentication protocols, with a detailed analysis of one called ELWSCAS, exposing its vulnerabilities. We propose a Secure and Efficient Lightweight Authentication Protocol (SELAP) to address ELWSCAS’s security flaws. Using two Raspberry Pis, we measure SELAP’s computation and communication costs, achieving 422 ms and 960 bits compared to ELWSCAS’s 548 ms and 2048 bits. Simulations with NS3 further validate SELAP’s performance, showing 276 successful authentications in 30 s, compared to ELWSCAS’s 202, with 17% less delay. These results demonstrate SELAP’s suitability for multi-sector IoT environments with varying conditions and high device density, delivering reliable performance.</div></div>","PeriodicalId":29968,"journal":{"name":"Internet of Things","volume":"30 ","pages":"Article 101499"},"PeriodicalIF":6.0,"publicationDate":"2025-01-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143223135","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}

引用次数: 0

Creating interpretable synthetic time series for enhancing the design and implementation of Internet of Things (IoT) solutions

IF 6 3区计算机科学

Internet of Things Pub Date : 2025-01-17 DOI: 10.1016/j.iot.2025.101500

Dimitris Gkoulis

引用次数: 0

Two-stage Stackelberg game based physical reality sensing incentive mechanism in metaverse

IF 6 3区计算机科学

Internet of Things Pub Date : 2025-01-17 DOI: 10.1016/j.iot.2025.101507

Nan Zhao , Lang Wan , Juan Wang , Zhe Chen , Dejun Li , Minyang Liu , Ruifeng Pan

引用次数: 0

Towards the underwater internet of things for subsea oil and gas monitoring

IF 6 3区计算机科学

Internet of Things Pub Date : 2025-01-16 DOI: 10.1016/j.iot.2025.101510

Craig Stewart , Beenish Ayaz , Nazila Fough , Radhakrishna Prabhu

{"title":"Towards the underwater internet of things for subsea oil and gas monitoring","authors":"Craig Stewart , Beenish Ayaz , Nazila Fough , Radhakrishna Prabhu","doi":"10.1016/j.iot.2025.101510","DOIUrl":"10.1016/j.iot.2025.101510","url":null,"abstract":"<div><div>The oil and gas industry (O&G) is at the forefront of significant social change as the world aligns itself with the 2050 Net Zero agenda. The Underwater Internet of Things (UIoT) will inevitably be part of this process across a broad range of applications from monitoring pollutant emissions to enabling proactive maintenance of pipelines through robotics. The goal of this survey is to discuss the current and future technologies of UIoT regarding communications, networking, sensing and computational technologies and how it will address challenges that the O&G is facing. Firstly, a brief discussion on subsea assets is carried out. After, the propagation and physical characteristics of common underwater communications technologies are discussed in depth and how they relate to effective transmission of data. Additionally, this discussion of communication is followed by another on networking through analysis of the three layers of the protocol stack most affected by the change in transmission media, the physical, datalink and network layers. After this discussion, an example investigation is presented on a simulated network to support O&G UIoT with a proceeding introduction to the available simulation tools for designing subsea networks. then, research challenges of the UIoT in the O&G are identified. Finally, the trends in UIoT research are discussed regarding their relevance to O&G as well as concluding remarks.</div></div>","PeriodicalId":29968,"journal":{"name":"Internet of Things","volume":"30 ","pages":"Article 101510"},"PeriodicalIF":6.0,"publicationDate":"2025-01-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143222789","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}

引用次数: 0

MicroFlow: An Efficient Rust-Based Inference Engine for TinyML

IF 6 3区计算机科学

Internet of Things Pub Date : 2025-01-15 DOI: 10.1016/j.iot.2025.101498

Matteo Carnelos , Francesco Pasti , Nicola Bellotto

{"title":"MicroFlow: An Efficient Rust-Based Inference Engine for TinyML","authors":"Matteo Carnelos , Francesco Pasti , Nicola Bellotto","doi":"10.1016/j.iot.2025.101498","DOIUrl":"10.1016/j.iot.2025.101498","url":null,"abstract":"<div><div>In recent years, there has been a significant interest in developing machine learning algorithms on embedded systems. This is particularly relevant for bare metal devices in Internet of Things, Robotics, and Industrial applications that face limited memory, processing power, and storage, and which require extreme robustness. To address these constraints, we present MicroFlow, an open-source TinyML framework for the deployment of Neural Networks (NNs) on embedded systems using the Rust programming language. The compiler-based inference engine of MicroFlow, coupled with Rust’s memory safety, makes it suitable for TinyML applications in critical environments. The proposed framework enables the successful deployment of NNs on highly resource-constrained devices, including bare-metal 8-bit microcontrollers with only 2 kB of RAM. Furthermore, MicroFlow is able to use less Flash and RAM memory than other state-of-the-art solutions for deploying NN reference models (i.e. wake-word and person detection), achieving equally accurate but faster inference compared to existing engines on medium-size NNs, and similar performance on bigger ones. The experimental results prove the efficiency and suitability of MicroFlow for the deployment of TinyML models in critical environments where resources are particularly limited.</div></div>","PeriodicalId":29968,"journal":{"name":"Internet of Things","volume":"30 ","pages":"Article 101498"},"PeriodicalIF":6.0,"publicationDate":"2025-01-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143222927","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

A systematic evaluation of white-box explainable AI methods for anomaly detection in IoT systems

IF 6 3区计算机科学

Internet of Things Pub Date : 2025-01-15 DOI: 10.1016/j.iot.2025.101505

Anna N. Gummadi , Osvaldo Arreche , Mustafa Abdallah

{"title":"A systematic evaluation of white-box explainable AI methods for anomaly detection in IoT systems","authors":"Anna N. Gummadi , Osvaldo Arreche , Mustafa Abdallah","doi":"10.1016/j.iot.2025.101505","DOIUrl":"10.1016/j.iot.2025.101505","url":null,"abstract":"<div><div>The rapid evolution of Internet of Things (IoT) systems has created new avenues for studying how artificial intelligence (AI) might be used to improve anomaly detection in these systems. Using AI for finding and predicting abnormalities or malfunctions in IoT devices with precision is crucial to highlight the salient characteristics of these anomalies to save maintenance expenses and downtime. Pairing these AI-based anomaly detection methods with explanations will make them applicable in real-world IoT systems. However, the systematic evaluation of various explainable AI (XAI) strategies used for anomaly detection in IoT systems is still lacking. To help address this gap, we propose an evaluation framework for different white-box XAI techniques in which we consider six evaluation metrics that are essential for evaluating XAI methods and have mappings to AI and security domains. In particular, we use these metrics to evaluate Integrated Gradients (IG), Layer-wise Relevance Propagation (LRP), and Deep Learning - SHAP (Deep-SHAP), through a single deep neural network (DNN) model. These metrics are descriptive accuracy, sparsity, stability, efficiency, robustness, and completeness. We used these six metrics to evaluate the three aforementioned white-box XAI methods considered in our work. Our evaluation framework is tested on two different IoT datasets: the N-BaIoT dataset, which focuses on detecting IoT botnet attacks on commercial IoT devices infected by the Mirai and Gafgyt botnets, and the MEMS dataset, which contains sensor data from manufacturing processes aimed at detecting abnormal readings. In the context of IoT systems, we illustrate the advantages and disadvantages of the state-of-the-art white-box XAI techniques through this study. Our evaluation framework’s source code is made accessible to the general public, providing a common benchmark for upcoming studies that explore applying explainable AI for enhancing anomaly detection in the emerging IoT field.</div></div>","PeriodicalId":29968,"journal":{"name":"Internet of Things","volume":"30 ","pages":"Article 101505"},"PeriodicalIF":6.0,"publicationDate":"2025-01-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143222926","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}

引用次数: 0

Incentivizing task offloading in IoT: A distributed auctions-based DRL approach

IF 6 3区计算机科学

Internet of Things Pub Date : 2025-01-12 DOI: 10.1016/j.iot.2025.101493

Soumeya Demil, Mohammed Riyadh Abdmeziem

{"title":"Incentivizing task offloading in IoT: A distributed auctions-based DRL approach","authors":"Soumeya Demil, Mohammed Riyadh Abdmeziem","doi":"10.1016/j.iot.2025.101493","DOIUrl":"10.1016/j.iot.2025.101493","url":null,"abstract":"<div><div>Federated Learning (FL) has emerged as a powerful tool for leveraging the vast quantities of data generated by Internet of Things (IoT) devices. Its advantage lies in its ability to preserve participants’ data privacy through keeping it local. In addition, FL alleviates the communication overhead in cloud-centric ML for IoT approaches, through sharing model updates instead of large raw data, which optimizes bandwidth use. Furthermore, decentralized FL has been useful in addressing security concerns typical in conventional settings. Nevertheless, in such zero-trust scenarios where there is no central coordinator, nodes exhibit reluctance to participate due to the lack of clear rewards and trust issues. Additionally, constrained FL clients may abandon their tasks, which negatively impacts learning performance. In this paper, we propose a double-incentive FL approach to address the dual challenge of node reluctance and task offloading in a fully distributed FL-based IoT network. We introduce an auction-based offloading scheme to handle task abandonment. Multi-Agent Deep Reinforcement Learning (MADRL) is leveraged to build a bidding strategy with long-term optimization of system and individual utilities. We also present a client filtering and rewarding algorithm based on a reputation model. Our objective is to promote truthfulness and enhance resilience against malicious nodes, while improving energy efficiency and accuracy. By employing a REINFORCE-based scheme for offloading, our approach demonstrates a superior trade-off between energy efficiency and accuracy, as well as resilience to malicious behavior. Furthermore, empirical results highlight its performance in terms of truthfulness, despite the uncertain and opaque nature of the environment.</div></div>","PeriodicalId":29968,"journal":{"name":"Internet of Things","volume":"30 ","pages":"Article 101493"},"PeriodicalIF":6.0,"publicationDate":"2025-01-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143222960","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}

引用次数: 0

Low latency and secure data encryption for multi-hop biometric authentication in distributed networks

IF 6 3区计算机科学

Internet of Things Pub Date : 2025-01-12 DOI: 10.1016/j.iot.2025.101501

Sun-Jin Lee, Jin-Min Lee, Il-Gu Lee

{"title":"Low latency and secure data encryption for multi-hop biometric authentication in distributed networks","authors":"Sun-Jin Lee, Jin-Min Lee, Il-Gu Lee","doi":"10.1016/j.iot.2025.101501","DOIUrl":"10.1016/j.iot.2025.101501","url":null,"abstract":"<div><div>Because of the rapid development of artificial intelligence and big data technology, biometric information has become widely used in applications across industries, such as biometric authentication and telemedicine. However, biometric information is unique and cannot be changed or restored once leaked. Therefore, the security and management of biometric information must be more thorough than those applied to other types of information. In particular, if end-to-end encryption is not maintained and decryption is performed for the sake of precise data learning or information protection at intermediate nodes, the security of the process becomes weaker, increasing the risk of data leakage. To solve such problems, research is currently being conducted on homomorphic encryption, in which calculation and learning can be performed in an encrypted state, for biometric recognition systems. However, homomorphic encryption requires a larger ciphertext size than those used in other encryption methods. Thus, this type of encryption has been limited by its large delay, which deteriorates the performance because of noise boost-up and thus does not guarantee quality of service in a multi-hop network environment. This study proposes a novel multi-hop encryption-based biometric information slicing method to improve latency and security in biometric authentication. Experimental results demonstrate an 83.23% latency reduction compared to a conventional single-process model in a Paillier-based homomorphic encryption environment. Additionally, it offers a 98.4% security improvement compared to the commonly used Advanced Encryption Standard for packet encryption. This approach effectively addresses the trade-off between security and latency, a critical aspect not fully explored in previous studies.</div></div>","PeriodicalId":29968,"journal":{"name":"Internet of Things","volume":"30 ","pages":"Article 101501"},"PeriodicalIF":6.0,"publicationDate":"2025-01-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143222885","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0