Internet of Things最新文献

{"title":"Detection of moving small targets in infrared images for urban traffic monitoring","authors":"Juan Wang ,&nbsp;Hao Yang ,&nbsp;Zizhen Zhang ,&nbsp;Nan Zhao ,&nbsp;Jixiang Shao ,&nbsp;Minghua Wu ,&nbsp;Zhigang Ma ,&nbsp;Jialu Zhu ,&nbsp;Xu An Wang ,&nbsp;Haina Song","doi":"10.1016/j.iot.2025.101673","DOIUrl":"10.1016/j.iot.2025.101673","url":null,"abstract":"<div><div>The Internet of Vehicles (IoV) and autonomous driving technologies require increasingly robust object detection capabilities, especially for small objects. However, reliably detecting small objects in urban traffic scenarios remains technically challenging under adverse weather conditions, including low illumination, rain, and snow. To address these challenges, we propose a fused IR–visible imaging approach using an enhanced YOLOv9 architecture. The proposed method employs a dual-branch semantic enhancement architecture, which achieves dynamic inter-modal feature weighting through a channel attention mechanism. The visible branch preserves texture details, while the infrared branch extracts thermal radiation characteristics, followed by multi-scale feature-level fusion. Firstly, we present UR-YOLO designed for detecting small targets in urban traffic environments. Secondly, we propose a novel DeeperFuse module that incorporates dual-branch semantic enhancement and channel attention mechanisms for effective multimodal feature fusion. Finally, by jointly optimizing fusion and detection losses, the method preserves critical details, enhances clarity and contrast. Experimental evaluation on the M^{relax special {t4ht=³}}FD dataset demonstrates improved detection performance relative to the baseline YOLOv9 model. The results show an increase of 1.4 percentage points in mAP (from 83.3% to 84.7%) and 2.2 percentage points in <span><math><mrow><mi>A</mi><msub><mrow><mi>P</mi></mrow><mrow><mi>s</mi><mi>m</mi><mi>a</mi><mi>l</mi><mi>l</mi></mrow></msub></mrow></math></span> (from 51.6% to 53.8%). Furthermore, our method achieves real-time processing at 30 FPS, making it suitable for deployment in urban autonomous driving scenarios. Future work will focus on enhancing model performance via multimodal fusion, lightweight design, and multi-scale feature learning. We will also develop diverse datasets to advance autonomous driving perception in complex environments.</div></div>","PeriodicalId":29968,"journal":{"name":"Internet of Things","volume":"33 ","pages":"Article 101673"},"PeriodicalIF":6.0,"publicationDate":"2025-06-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144481478","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":"Federated learning for anomaly detection on Internet of Medical Things: A survey","authors":"Rui P. Pinto,&nbsp;Bruno M.C. Silva,&nbsp;Pedro R.M. Inácio","doi":"10.1016/j.iot.2025.101677","DOIUrl":"10.1016/j.iot.2025.101677","url":null,"abstract":"<div><div>The Internet of Medical Things (IoMT), an extension of the Internet of Things (IoT) paradigm where interconnected medical devices can sense and act within healthcare environments, aims to improve patient comfort, optimize outcomes and streamline medical processes. IoMT has seen significant growth in recent years, transforming healthcare with advanced monitoring, diagnostics, and data-sharing capabilities, though it also faces security and privacy challenges. The widespread attack surface of IoMT, combined with the difficulty of embedding robust security mechanisms in resource-constrained medical devices, makes IoMT systems particularly attractive targets for cyberattacks and a source of numerous security challenges. Anomaly detection systems are frequently part of the solution for IoMT cybersecurity, but they face unique integration challenges, especially in environments where patient data privacy is paramount. Federated Learning (FL) offers a promising approach to address these privacy concerns by enabling distributed training without sharing raw data. This paper provides a comprehensive literature review of FL applications in anomaly detection within IoMT ecosystems. It describes recent implementations, highlights the main open issues, and identifies future research challenges. This work elucidates the feasibility and challenges of FL-based anomaly detection systems applied to IoMT, offering insights for advancing IoMT security.</div></div>","PeriodicalId":29968,"journal":{"name":"Internet of Things","volume":"33 ","pages":"Article 101677"},"PeriodicalIF":6.0,"publicationDate":"2025-06-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144481480","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 secure energy management model for Peer-to-Peer smart grids with user-centric constraints","authors":"Waqas Amin ,&nbsp;Qi Huang ,&nbsp;Jian Li ,&nbsp;Abdullah Aman Khan ,&nbsp;Umashankar Subramaniam ,&nbsp;Sivakumar Selvam","doi":"10.1016/j.iot.2025.101678","DOIUrl":"10.1016/j.iot.2025.101678","url":null,"abstract":"<div><div>In today’s smart grid era, ensuring fair energy distribution while protecting participants’ data privacy is a critical challenge, particularly in Peer-to-Peer (P2P) energy trading environments. To address this challenge, this paper presents a privacy-preserving energy management model that ensures fair energy allocation based on participants’ reported information. By identifying the demand-to-supply ratio, the proposed model classifies the market operation mode either buyers’ mode or sellers’ mode and manages energy accordingly. The model employs a quorum-based architecture that integrates SHA-256 encryption and Shamir’s Secret Sharing scheme to safeguard participants’ private data against potential cyber-attacks such as Man-in-the-Middle (MitM) and False Data Injection Attacks (FDIA). Simulation results demonstrate that once the system operator receives the valid threshold shares, the original information can be successfully reconstructed. Furthermore, the simulation also indicates that the proposed model not only improves grid stress by up to 76.60% during peak hours but also transforms the grid’s role from an energy taker to an energy contributor.</div></div>","PeriodicalId":29968,"journal":{"name":"Internet of Things","volume":"33 ","pages":"Article 101678"},"PeriodicalIF":6.0,"publicationDate":"2025-06-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144481477","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":"XBiDeep: A novel explainable artificial intelligence based intrusion detection system for Internet of Medical Things environment","authors":"Zeynep Turgut,&nbsp;Muhammet Sinan Başarslan","doi":"10.1016/j.iot.2025.101675","DOIUrl":"10.1016/j.iot.2025.101675","url":null,"abstract":"<div><div>In this study, an IDS XBiDeep based on the use of deep learning architectures for the IoMT - IoHT environments is proposed. To evaluate the performance of the proposed technique, three different datasets collected from IoMT environments: CICIoMT2024, IoMT-TrafficData, and ECU-IoHT are used. CICIoMT2024 and ECU-IoHT possess imbalanced data structures, while IoMT-TrafficData contains a balanced structure, allowing the effectiveness of the model to be examined across both balanced and imbalanced datasets. Rather than performing a simple binary classification between attack and benign data, multi-class classification is conducted to investigate various attack types. To achieve high performance across all IoMT datasets, RNN, LSTM, GRU, BiLSTM, and BiGRU architectures are tested individually and in hybrid configurations. The best results are observed with the hybrid BiGRU-BiLSTM model, which is subsequently integrated into the proposed XBiDeep architecture. Specifically, it reached 0.9975 accuracy for 6-class classification and 0.9985 for 19-class classification on the CICIoMT2024 dataset. On the IoMT-TrafficData dataset, the model attained 0.9990 accuracy, while 0.9987 accuracy was obtained on the ECU-IoHT dataset. The outcomes of the created architecture are analyzed using XAI models: SHAP and LIME. The SHAP analysis identifies key features distinguishing different attack types from benign data, while the LIME analysis highlights the most effective features for detecting each specific attack type. Importance of features is revealed both locally and globally, based on attack types and across the entire system. Hence, this study introduces an explainable deep learning-based IDS with high accuracy across diverse IoMT datasets and attacks.</div></div>","PeriodicalId":29968,"journal":{"name":"Internet of Things","volume":"33 ","pages":"Article 101675"},"PeriodicalIF":6.0,"publicationDate":"2025-06-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144481479","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":"Towards robust stability prediction in smart grids: GAN-based approach under data constraints and adversarial challenges","authors":"Emad Efatinasab ,&nbsp;Alessandro Brighente ,&nbsp;Denis Donadel ,&nbsp;Mauro Conti ,&nbsp;Mirco Rampazzo","doi":"10.1016/j.iot.2025.101662","DOIUrl":"10.1016/j.iot.2025.101662","url":null,"abstract":"<div><div>Smart grids are crucial for meeting rising energy demands driven by global population growth and urbanization. By integrating renewable energy sources, they enhance efficiency, reliability, and sustainability. However, ensuring their availability and security requires advanced operational control and safety measures. Although artificial intelligence and machine learning can help assess grid stability, challenges such as data scarcity and cybersecurity threats, particularly adversarial attacks, remain. Data scarcity is a major issue, as obtaining real-world instances of grid instability requires significant expertise, resources, and time. Yet, these instances are critical for testing new research advancements and security mitigations. This paper introduces a novel framework for detecting instability in smart grids using only stable data. It employs a Generative Adversarial Network (GAN) where the generator is designed not to produce near-realistic data but instead to generate Out-Of-Distribution (OOD) samples with respect to the stable class. These OOD samples represent unstable behavior, anomalies, or disturbances that deviate from the stable data distribution. By training exclusively on stable data and exposing the discriminator to OOD samples, our framework learns a robust decision boundary to distinguish stable conditions from any unstable behavior, without requiring unstable data during training. Furthermore, we incorporate an adversarial training layer to enhance resilience against attacks. Evaluated on a real-world dataset, our solution achieves up to 98.1% accuracy in predicting grid stability and 98.9% in detecting adversarial attacks. Implemented on a single-board computer, it enables real-time decision-making with an average response time of under 7ms.</div></div>","PeriodicalId":29968,"journal":{"name":"Internet of Things","volume":"33 ","pages":"Article 101662"},"PeriodicalIF":6.0,"publicationDate":"2025-06-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144365148","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":"Key–value data collection with local differential privacy for urban air quality monitoring in crowdsensing","authors":"Yanming Fu ,&nbsp;Haodong Lu ,&nbsp;Jiayuan Chen ,&nbsp;Binyang Luo","doi":"10.1016/j.iot.2025.101670","DOIUrl":"10.1016/j.iot.2025.101670","url":null,"abstract":"<div><div>The growth of IoT and mobile devices has led to Mobile Crowdsensing (MCS), a cost-effective data collection method crucial for smart cities. While MCS outperforms wireless sensor networks, it may expose workers’ sensitive data, such as location and identity, in air quality monitoring. Traditional privacy-preserving techniques, such as location obfuscation and data perturbation, have inherent limitations in ensuring strong privacy protection. Moreover, the frequent uploading of numerical data during task execution requires a larger privacy budget, thereby increasing the risk of privacy leakage. To solve these problems, this paper proposes a key–value data collection scheme based on local differential privacy for air quality monitoring in smart cities. The proposed scheme aims to protect user privacy while ensuring data utility. It consists of two main phases: data collection and data prediction. During the data collection phase, workers locally perturb both the task location (key) and the sensed data (value), utilizing the correlation between keys and values to enhance data utility. The system subsequently aggregates the perturbed data and applies bias correction to ensure unbiased estimation. In the prediction phase, an exponential smoothing technique is introduced to mitigate the impact of privacy-preserving mechanisms on prediction accuracy. This method effectively reduces random fluctuations in the data, thereby enhancing the overall prediction performance. Experiments on real-world datasets show that the proposed scheme outperforms other privacy-preserving algorithms in efficiency while maintaining nearly the same prediction accuracy as non-privacy-preserving methods, effectively balancing privacy and data utility.</div></div>","PeriodicalId":29968,"journal":{"name":"Internet of Things","volume":"33 ","pages":"Article 101670"},"PeriodicalIF":6.0,"publicationDate":"2025-06-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144321702","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 multi-layer echo state network for efficient DDoS detection in resource-constrained environments","authors":"M. Franckie Singha,&nbsp;Ripon Patgiri,&nbsp;Laiphrakpam Dolendro Singh","doi":"10.1016/j.iot.2025.101665","DOIUrl":"10.1016/j.iot.2025.101665","url":null,"abstract":"<div><div>This study proposes a multi-layer Echo State Network (ESN) model for effectively detecting DDoS attacks on resource-constrained and low-memory devices. Generally, these low-memory devices, common in smart homes, healthcare, and industrial applications, do not have enough computational resources to run traditional deep learning methods of DDoS attack detection. This makes the devices much more vulnerable to attacks. While previous works have focused mainly on improving detection accuracy, they have failed to consider vital trade-offs between resource utilization and detection performance. The proposed ESN model achieves 99.33% and 99.99% accuracy in CICDDoS2019 and CICIoT2023 datasets respectively. With only 640 trainable parameters, it ensures high performance with minimum consumption of computational resources. The proposed model has 1.27% and 0.06% CPU utilization in CICDDoS2019 and CICIoT2023. The CPU utilization is much lesser compared to LSTM, RNN, CNN, and state-of-the-art models, respectively. This makes our model a lightweight architecture suitable for devices with limited memory and processing power. The paper presents an efficient, lightweight model for the security of low-resource environments and robust DDoS detection without loss of accuracy.</div></div>","PeriodicalId":29968,"journal":{"name":"Internet of Things","volume":"33 ","pages":"Article 101665"},"PeriodicalIF":6.0,"publicationDate":"2025-06-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144313990","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":"Age of Information minimization for secure data collection in multi UAV-assisted IoT applications","authors":"Nazli Tekin","doi":"10.1016/j.iot.2025.101672","DOIUrl":"10.1016/j.iot.2025.101672","url":null,"abstract":"<div><div>Unmanned Aerial Vehicles (UAVs) have emerged as a promising solution for data collection in Internet of Things (IoT) applications, particularly in scenarios where traditional infrastructure is unavailable or unreliable. Minimizing the Age of Information (AoI) while providing secure data collection is crucial for delay-sensitive applications. However, secure data collection introduces considerable energy and delay overhead for both resource-constrained IoT devices and UAVs. This paper proposes a novel mixed integer programming (MIP) model to minimize the average AoI with the minimum number of UAVs. Unlike previous works that largely disregard the costs of security and energy constraints of IoT, the proposed model integrates the delay and energy costs of encryption methods on both UAVs and IoT devices. Colonial Selection Algorithm (CSA) is developed as a metaheuristic to overcome the computational complexity of the MIP model in large-scale IoT applications. The impact of attribute-based encryption (ABE) methods, such as Key-Policy ABE (KP-ABE) and Ciphertext-Policy ABE (CP-ABE), on AoI minimization and the minimum number of required UAVs is analyzed. The results demonstrate that the KB-ABE-YCT method performs better in minimizing AoI with fewer UAVs.</div></div>","PeriodicalId":29968,"journal":{"name":"Internet of Things","volume":"33 ","pages":"Article 101672"},"PeriodicalIF":6.0,"publicationDate":"2025-06-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144365149","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":"TL2AB : Trusted lightweight authentication using AI and blockchain for 6G networks","authors":"Sabrina Sakraoui ,&nbsp;Makhlouf Derdour ,&nbsp;Ahmed Ahmim ,&nbsp;Reham Almukhlifi ,&nbsp;Marwa Ahmim ,&nbsp;Insaf Ullah","doi":"10.1016/j.iot.2025.101661","DOIUrl":"10.1016/j.iot.2025.101661","url":null,"abstract":"<div><div>The upcoming era of Sixth-Generation technology brings about special opportunities and challenges with respect to cybersecurity, especially regarding secure authentication mechanisms. This paper introduces TL2AB, a trusted lightweight authentication framework using artificial intelligence and blockchain technology. The proposed solution addresses critical security and privacy issues related to 6G applications, particularly in sensitive sectors such as healthcare and IoT. TL2AB enhances security in communication by introducing a new three-factor authentication scheme while allowing users to access rapidly and efficiently. TL2AB not only meets the high demands of 6G networks but also creates a robust foundation for future research in secure authentication frameworks.</div></div>","PeriodicalId":29968,"journal":{"name":"Internet of Things","volume":"33 ","pages":"Article 101661"},"PeriodicalIF":6.0,"publicationDate":"2025-06-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144513944","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 comprehensive and realistic performance evaluation of post-quantum security for consumer IoT devices","authors":"Yacoub Hanna ,&nbsp;Jessica Bozhko ,&nbsp;Samet Tonyali ,&nbsp;Ricardo Harrilal-Parchment ,&nbsp;Mumin Cebe ,&nbsp;Kemal Akkaya","doi":"10.1016/j.iot.2025.101650","DOIUrl":"10.1016/j.iot.2025.101650","url":null,"abstract":"<div><div>The computational capacity envisaged for quantum computers poses a significant threat to today’s traditional cryptographic algorithms. Although they are not yet large enough to compromise current cryptographic protocols, one can practice retrospective decryption since data packets traveling through the Internet can be easily sniffed. This threat extends to wireless communication security within consumer IoT devices that use lightweight cryptography due to limited computational power. Thus, countermeasures against potential quantum attacks should be preemptively adopted. NIST is leading efforts to standardize several algorithms as quantum-resistant Key Exchange Mechanisms (KEMs) and Digital Signatures. In this paper, we investigate the viability of these Post-Quantum algorithms in the Transport Layer Security (TLS) of power-constrained IoT devices. Specifically, it focuses on two widely used IoT network protocol stacks, i.e., Bluetooth Low Energy (BLE) and Wi-Fi. To this end, we build a realistic IoT testbed running IP over BLE. Our evaluation considers the impact of several realistic factors for the first time, such as using a chain of certificates on the server side and incorporating certificate validation methods such as Online Certificate Status Protocol (OCSP) and Certificate Revocation Lists (CRL). We also evaluate the impact of mutual authentication between the server and the client. Utilizing the outcomes of this evaluation, we then propose a novel approach for IoT devices to dynamically choose the most efficient KEM algorithm for TLS based on the device’s physical network interface. The performance results provide valuable insights with respect to the TLS latency and energy consumption of consumer IoT devices.</div></div>","PeriodicalId":29968,"journal":{"name":"Internet of Things","volume":"33 ","pages":"Article 101650"},"PeriodicalIF":6.0,"publicationDate":"2025-06-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144306578","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}