Internet of Things最新文献

{"title":"FedWDP: A Wasserstein-distance-based federated learning for privacy and heterogeneous data in IoT","authors":"Jinlong Bai,&nbsp;Lifeng Cao,&nbsp;Jinhui Li,&nbsp;Jiling Wan,&nbsp;Xuehui Du","doi":"10.1016/j.iot.2025.101532","DOIUrl":"10.1016/j.iot.2025.101532","url":null,"abstract":"<div><div>As the need for interconnected devices and data exchange grows in the Internet of Things (IoT), traditional centralized data processing methods increasingly struggle to maintain privacy and adapt to the diverse and dispersed nature of IoT devices. Federated learning, a decentralized approach to machine learning, presents a viable solution to these challenges. Yet, the varied nature of IoT data and stringent privacy requirements introduce unique obstacles for federated learning. This paper introduces FedWDP, a federated learning method specifically designed for IoT privacy needs and heterogeneous data. FedWDP uses the Wasserstein distance to quantify the gap between local and global parameters, integrating this measure as a regularization term in the loss function to reduce model discrepancies and improve accuracy. To further balance privacy and usability, an exponential decay strategy is implemented, allowing for adaptive distribution of differential privacy noise. For better performance on high-dimensional data, PCA-FedWDP is proposed, which combines principal component analysis (PCA) with differentially private federated learning to perform dimensionality reduction. Experimental results on non-IID datasets reveal that this approach significantly enhances both accuracy and availability for heterogeneous data while safeguarding user privacy. This study thus provides a valuable framework for applying federated learning in IoT settings, contributing to the secure and intelligent use of IoT data in both theoretical and practical contexts.</div></div>","PeriodicalId":29968,"journal":{"name":"Internet of Things","volume":"31 ","pages":"Article 101532"},"PeriodicalIF":6.0,"publicationDate":"2025-02-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143454509","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}

{"title":"A crossover-integrated Marine Predator Algorithm for feature selection in intrusion detection systems within IoT environments","authors":"Sharif Naser Makhadmeh ,&nbsp;Salam Fraihat ,&nbsp;Mohammed Awad ,&nbsp;Yousef Sanjalawe ,&nbsp;Mohammed Azmi Al-Betar ,&nbsp;Mohammed A. Awadallah","doi":"10.1016/j.iot.2025.101536","DOIUrl":"10.1016/j.iot.2025.101536","url":null,"abstract":"<div><div>In recent times, there has been a significant rise in cyberattacks targeting the Internet of Things (IoT) and cyberspace in general. Detecting intrusions in a time series environment is a critical challenge for Network Intrusion Detection Systems (NIDS). Building an effective NIDS requires carefully establishing an efficient model, with machine learning (ML) playing a prominent role. The performance of ML models depends on selecting the most informative feature subset. Recently, metaheuristic (MH) optimization methods have been effective in identifying these key features. However, standard MH methods require adjustment to incorporate NIDS-specific knowledge for optimal results, improving both MH performance and ML accuracy. This paper introduces a novel NIDS framework based on three key phases: preprocessing, optimization, and generalization. In the preprocessing phase, several datasets undergo cleaning and under-sampling. In the optimization phase, an enhanced version of the Marine Predators Algorithm (MPA) is proposed, utilizing the crossover operator to identify the most relevant features. The proposed method is called MPAC. The crossover operator is utilized to boost the exploitation capabilities of the MPA and find the optimal local solution for the NIDS. Finally, the selected features are applied to the NIDS. Eight different datasets are employed for examination and evaluation using different evaluation measurements to assess the effectiveness of the proposed NIDS. The experimental evaluation is organized into three phases: evaluating the proposed crossover modification by applying it to five algorithms and comparing results to the originals, comparing the results of the proposed algorithms to prove the robust performance of the MPAC, and comparing the results obtained by the MPAC with the stat-of-the-arts. The proposed MPAC confirmed its demonstration and high performance in detecting network attacks, wherein in the first evaluation phase, the proposed approach obtained better results in almost 90% of the comparisons. In the second comparison phase, the proposed MPAC achieved better results in six datasets out of eight, and in the last phase, the MPAC outperforms all compared methods.</div></div>","PeriodicalId":29968,"journal":{"name":"Internet of Things","volume":"31 ","pages":"Article 101536"},"PeriodicalIF":6.0,"publicationDate":"2025-02-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143437249","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":"Bridging FANETs and MANETs for synchronous data collection in precision agriculture activities using AirPro-FL: An energy aware fuzzy logic routing protocol","authors":"Georgios Kakamoukas ,&nbsp;Anastasios Economides ,&nbsp;Stamatia Bibi ,&nbsp;Panagiotis Sarigiannidis","doi":"10.1016/j.iot.2025.101535","DOIUrl":"10.1016/j.iot.2025.101535","url":null,"abstract":"<div><div>The use of Flying Ad-hoc Networks (FANETs) in precision agriculture requires the development of advanced routing protocols to manage UAV-specific challenges effectively. This paper presents AirPro-FL, a proactive routing protocol that uses fuzzy logic to optimize UAV performance in precision agriculture tasks. Unlike conventional FANET research, which often relies on stochastic mobility models that do not accurately reflect real-world agricultural missions, AirPro-FL is designed to address these gaps by enhancing UAV cooperation in scanning operations such as crop scouting, crop surveying and mapping, spraying applications, and geofencing. Traditionally, these agricultural activities rely on a single UAV, often resulting in inefficiencies. The UAV’s limited real-time data transmission capabilities, vulnerability to operational failures, and potential mission execution delays contribute to reduced overall effectiveness. The proposed system involving multiple UAVs significantly speeds up mission completion and enables real-time data transfer through the cooperation between FANETs and Mobile Ad-hoc Networks (MANETs). This innovation empowers agricultural stakeholders to make faster and more reliable decisions based on accurate data collection. Simulation results indicate that AirPro-FL consistently achieves the highest Packet Delivery Ratio (PDR) across all scenarios, halves the average end-to-end delay compared to the second-best protocol, and exhibits superior energy efficiency. The protocol’s success in optimizing data collection during scanning operations underscores its broader applicability beyond agriculture, extending to other fields such as environmental monitoring, disaster management, and surveillance, where similar mobility patterns are employed.</div></div>","PeriodicalId":29968,"journal":{"name":"Internet of Things","volume":"31 ","pages":"Article 101535"},"PeriodicalIF":6.0,"publicationDate":"2025-02-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143430074","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}

{"title":"Similarity-driven truncated aggregation framework for privacy-preserving short term load forecasting","authors":"Ahsan Raza Khan,&nbsp;Mohammad Al-Quraan,&nbsp;Lina Mohjazi,&nbsp;David Flynn,&nbsp;Muhammad Ali Imran,&nbsp;Ahmed Zoha","doi":"10.1016/j.iot.2025.101530","DOIUrl":"10.1016/j.iot.2025.101530","url":null,"abstract":"<div><div>Accurate short-term load forecasting (STLF) is essential for the efficient and reliable operation of power systems, enabling effective scheduling and integration of renewable energy sources. Federated learning (FL) offers a collaborative, privacy-preserving approach for distributed model training by avoiding data sharing among sources. However, existing FL methods for STLF often rely on clustering techniques for highly variable residential data, which struggle to effectively handle data diversity, privacy constraints, and anomalous model updates. This study addresses these concerns and presents a similarity-driven truncated aggregation (SDTA) algorithm designed for STLF at macro-level sub-stations. SDTA enhances model alignment by computing layer-wise cosine similarity among client updates and mitigates outliers through truncated mean aggregation, reducing overfitting and improving robustness. The algorithm integrates differential privacy (DP) mechanisms to protect model updates and applies cosine-similarity-based filtering to safeguard against adversarial attacks. Extensive simulations on real-world substation data validate that SDTA significantly outperforms standard FL algorithms such as federated averaging (FedAVG) and federated distance (FedDist). Under conditions without privacy constraints, SDTA achieves a mean absolute percentage error (MAPE) of 2.63%, surpassing FedAVG and FedDist with MAPE of 2.89% and 3.11%, respectively, with faster convergence. Under strict DP constraints, SDTA maintains high forecasting performance with a MAPE of 4.02%, outperforming FedDist and FedAVG by 9.7% and 20.4%, respectively. Furthermore, SDTA exhibits substantial resilience under adversarial conditions, achieving a MAPE reduction of 20.5% over FedAVG when 40% of edge nodes are compromised. Moreover, the study examines the robustness of SDTA against random client selection scenarios, illustrating its resilience and practical applicability in real-world settings, particularly when client selection rates are below 60%.</div></div>","PeriodicalId":29968,"journal":{"name":"Internet of Things","volume":"31 ","pages":"Article 101530"},"PeriodicalIF":6.0,"publicationDate":"2025-02-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143420136","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}

{"title":"Vortex Feature Positioning: Bridging tabular IIoT data and image-based deep learning","authors":"Jong-Ik Park ,&nbsp;Sihoon Seong ,&nbsp;JunKyu Lee ,&nbsp;Cheol-Ho Hong","doi":"10.1016/j.iot.2025.101533","DOIUrl":"10.1016/j.iot.2025.101533","url":null,"abstract":"<div><div>Tabular data from IIoT devices are typically analyzed using decision tree-based machine learning techniques, which struggle with high-dimensional and numeric data. To overcome these limitations, techniques converting tabular data into images have been developed, leveraging the strengths of image-based deep learning approaches such as Convolutional Neural Networks. These methods cluster similar features into distinct image areas with fixed sizes, regardless of the number of features, resembling actual photographs. However, this increases the possibility of overfitting, as similar features, when selected carefully in a tabular format, are often discarded to prevent this issue. Additionally, fixed image sizes can lead to wasted pixels with fewer features, resulting in computational inefficiency. We introduce Vortex Feature Positioning (VFP) to address these issues. VFP arranges features based on their correlation, spacing similar ones in a vortex pattern from the image center, with the image size determined by the attribute count. VFP outperforms traditional machine learning methods and existing conversion techniques in tests across seven datasets with varying real-valued attributes.</div></div>","PeriodicalId":29968,"journal":{"name":"Internet of Things","volume":"31 ","pages":"Article 101533"},"PeriodicalIF":6.0,"publicationDate":"2025-02-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143430075","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":"Accurate low-delay QRS detection algorithm for real-time ECG acquisition in IoT sensors","authors":"Sebin Kim ,&nbsp;Chaehyun Kim ,&nbsp;Youngwoo Yoo ,&nbsp;Young-Joon Kim","doi":"10.1016/j.iot.2025.101537","DOIUrl":"10.1016/j.iot.2025.101537","url":null,"abstract":"<div><div>QRS detection is crucial for heart function diagnosis and sports science. This paper presents a real-time QRS detection algorithm designed for low-cost wearable embedded platforms, enabling novel applications such as closed-loop stimulation for acute diseases, precise monitoring in sports science, and home health monitoring. This algorithm locates the R-peak in real-time, with a mean delay of 0.405 s, throughout the MIT-BIH dataset. We achieve high accuracy with minimal compromise to computational power or delay, using a two-step, find and validate method. Initially, we identify potential QRS candidates by detecting zero-crossing points through filtering and convolution processes. Next, we validate these candidates by comparing them with previous R-R intervals (RRI), adaptively comparing values to minimize T-wave errors and reject adjacent noise components. We introduced a novel algorithm based on RRI periodicity, simplifying the computational load while enhancing detection accuracy. By using the MIT-BIH dataset, we detected the QRS complexes with a 99.75% accuracy. Furthermore, we embedded the algorithm into an Arm Cortex-M4 microcontroller unit (MCU) with a 64 MHz clock, maintaining identical accuracy. We demonstrate live-stream QRS detection by generating MIT-BIH waveforms using a function generator and processing them with the MCU's on-chip 10-bit analog-to-digital converter (ADC), achieving 99.71% accuracy. Finally, we validate our work with a miniaturized flexible electrocardiogram (ECG) sensor in a form factor of a bandage, wirelessly linked to a smartwatch for real-time ECG monitoring and R-peak detection. A cloud connectivity network is established concluding that this work is suitable for practical monitoring applications.</div></div>","PeriodicalId":29968,"journal":{"name":"Internet of Things","volume":"31 ","pages":"Article 101537"},"PeriodicalIF":6.0,"publicationDate":"2025-02-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143420137","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":"Extending battery lifespan in IoT extreme sensor networks through collaborative reinforcement learning-powered task offloading","authors":"Mateo Cumia,&nbsp;Gabriel Mujica,&nbsp;Jorge Portilla","doi":"10.1016/j.iot.2025.101534","DOIUrl":"10.1016/j.iot.2025.101534","url":null,"abstract":"<div><div>The use of wireless sensor networks (WSN) is increasingly widespread in the Internet of Things domain. Additionally, since the onset of the edge computing paradigm that brings the cloud closer to devices, these networks have seen improvements in battery lifetime and processing time, particularly in extreme edge architectures where network resources are more limited. Meanwhile, AI and machine learning techniques have been expanding across various domains to optimize different decision-making processes, including the task assignment problem in computation offloading. This article employs reinforcement learning (RL) techniques to address the task offloading problem, aiming to extend the lifespan of a WSN. To achieve this, a distributed multi-agent Q-learning algorithm is proposed, where sensor nodes (SNs) make collaborative decisions towards a common goal, avoiding selfish decision-making. The proposed algorithm is compared with two other state-of-the-art solutions, that is, a well-known Q-learning algorithm that allows centralized estimation of the Q-table before distributing it to the network’s sensor nodes (SNs), and a similar implementation of this algorithm but using Deep Q-learning, which theoretically should achieve the best results. The outcomes show that the multi-agent RL algorithm improves performance when it takes other nodes in the network into account in its decisions, being the most suitable solution to be embedded in resource-constrained devices. Although it still achieves worse results than the Deep Q-learning algorithm, the latter involves much greater difficulties for implementation in real devices.</div></div>","PeriodicalId":29968,"journal":{"name":"Internet of Things","volume":"31 ","pages":"Article 101534"},"PeriodicalIF":6.0,"publicationDate":"2025-02-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143420135","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}

{"title":"Visual-based obstacle avoidance method using advanced CNN for mobile robots","authors":"Oğuz Misir ,&nbsp;Muhammed Celik","doi":"10.1016/j.iot.2025.101538","DOIUrl":"10.1016/j.iot.2025.101538","url":null,"abstract":"<div><div>Artificial intelligence is one of the key factors accelerating the development of cyber-physical systems. Autonomous robots, in particular, heavily rely on deep learning technologies for sensing and interpreting their environments. In this context, this paper presents an extended MobileNetV2-based obstacle avoidance method for mobile robots. The deep network architecture used in the proposed method has a low number of parameters, making it suitable for deployment on mobile devices that do not require high computational power. To implement the proposed method, a two-wheeled non-holonomic mobile robot was designed. This mobile robot was equipped with a Jetson Nano development board to utilize deep network architectures. Additionally, camera and ultrasonic sensor data were used to enable the mobile robot to detect obstacles. To test the performance of the proposed method, three different obstacle-filled environments were designed to simulate real-world conditions. A unique dataset was created by combining images with sensor data collected from the environment. This dataset was generated by adding light and dark shades of red, blue, and green to the camera images, correlating the color intensity with the obstacle distance measured by the ultrasonic sensor. The extended MobileNetV2 architecture, developed for the obstacle avoidance task, was trained on this dataset and compared with state-of-the-art low-parameter Convolutional Neural Network (CNN) models. Based on the results, the proposed deep learning architecture outperformed the other models, achieving 92.78 % accuracy. Furthermore, the mobile robot successfully completed the obstacle avoidance task in real-world applications.</div></div>","PeriodicalId":29968,"journal":{"name":"Internet of Things","volume":"31 ","pages":"Article 101538"},"PeriodicalIF":6.0,"publicationDate":"2025-02-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143403660","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":"Trust dynamicity for IoT: How do i trust your social IoT cluster?","authors":"Davide Ferraris ,&nbsp;Carmen Fernandez-Gago ,&nbsp;Younes Assouyat ,&nbsp;Houda Labiod ,&nbsp;Wang Haiguang ,&nbsp;Javier Lopez","doi":"10.1016/j.iot.2025.101529","DOIUrl":"10.1016/j.iot.2025.101529","url":null,"abstract":"<div><div>The Social IoT (SIoT) enhances the traditional Internet of Things (IoT) by integrating social relationships between device owners. This paper presents a dynamic trust framework specifically designed for SIoT environments, with the objective of providing security against malicious attacks targeting IoT devices. The framework offers a multi-dimensional analysis of trust, emphasizing the behaviours and contextual interactions of domestic devices. A prototype implementing the proposed framework is introduced and evaluated across three different use cases showing how to assess device reputation, enable dynamic device integration, and secure communication within device clusters. The evaluation results highlight the framework’s ability to enhance the reliability of device interactions and ensure seamless interoperability among devices utilizing different trust models. This significant improvement in trust management contributes to more secure and efficient SIoT operations. The findings underscore the critical role of dynamic trust adaptation and interoperability in creating a cohesive and secure SIoT ecosystem.</div></div>","PeriodicalId":29968,"journal":{"name":"Internet of Things","volume":"30 ","pages":"Article 101529"},"PeriodicalIF":6.0,"publicationDate":"2025-02-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143387945","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":"Application of deep reinforcement learning for intrusion detection in Internet of Things: A systematic review","authors":"Saeid Jamshidi ,&nbsp;Amin Nikanjam ,&nbsp;Kawser Wazed Nafi ,&nbsp;Foutse Khomh ,&nbsp;Rasoul Rasta","doi":"10.1016/j.iot.2025.101531","DOIUrl":"10.1016/j.iot.2025.101531","url":null,"abstract":"<div><div>The Internet of Things (IoT) has significantly expanded the digital landscape, interconnecting an unprecedented array of devices, from home appliances to industrial equipment. This growth enhances functionality, e.g., automation, remote monitoring, and control, and introduces substantial security challenges, especially in defending these devices against cyber threats. Intrusion Detection Systems (IDS) are crucial for securing IoT; however, traditional IDS often struggle to adapt to IoT networks’ dynamic and evolving nature and threat patterns. A potential solution is using Deep Reinforcement Learning (DRL) to enhance IDS adaptability, enabling them to learn from and react to their operational environment dynamically.</div><div>This systematic review examines the application of DRL to enhance IDS in IoT settings, covering research from the past ten years. This review underscores the state-of-the-art DRL techniques employed to improve adaptive threat detection and real-time security across IoT domains by analyzing various studies. Our findings demonstrate that DRL significantly enhances IDS capabilities by enabling systems to learn and adapt from their operational environment. This adaptability allows IDS to improve threat detection accuracy and minimize false positives, making them more effective in identifying genuine threats while reducing unnecessary alerts. Additionally, this systematic review identifies critical research gaps and future research directions, emphasizing the necessity for more diverse datasets, enhanced reproducibility, and improved integration with emerging IoT technologies. This review aims to foster the development of dynamic and adaptive IDS solutions essential for protecting IoT networks against sophisticated cyber threats.</div></div>","PeriodicalId":29968,"journal":{"name":"Internet of Things","volume":"31 ","pages":"Article 101531"},"PeriodicalIF":6.0,"publicationDate":"2025-02-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143403673","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}