Internet of Things最新文献_第6页

Extending battery lifespan in IoT extreme sensor networks through collaborative reinforcement learning-powered task offloading

IF 6 3区计算机科学

Internet of Things Pub Date : 2025-02-11 DOI: 10.1016/j.iot.2025.101534

Mateo Cumia, Gabriel Mujica, Jorge Portilla

{"title":"Extending battery lifespan in IoT extreme sensor networks through collaborative reinforcement learning-powered task offloading","authors":"Mateo Cumia, Gabriel Mujica, 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}

引用次数: 0

Visual-based obstacle avoidance method using advanced CNN for mobile robots

IF 6 3区计算机科学

Internet of Things Pub Date : 2025-02-11 DOI: 10.1016/j.iot.2025.101538

Oğuz Misir , Muhammed Celik

{"title":"Visual-based obstacle avoidance method using advanced CNN for mobile robots","authors":"Oğuz Misir , 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}

引用次数: 0

Trust dynamicity for IoT: How do i trust your social IoT cluster?

IF 6 3区计算机科学

Internet of Things Pub Date : 2025-02-11 DOI: 10.1016/j.iot.2025.101529

Davide Ferraris , Carmen Fernandez-Gago , Younes Assouyat , Houda Labiod , Wang Haiguang , Javier Lopez

{"title":"Trust dynamicity for IoT: How do i trust your social IoT cluster?","authors":"Davide Ferraris , Carmen Fernandez-Gago , Younes Assouyat , Houda Labiod , Wang Haiguang , 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}

引用次数: 0

Application of deep reinforcement learning for intrusion detection in Internet of Things: A systematic review

IF 6 3区计算机科学

Internet of Things Pub Date : 2025-02-11 DOI: 10.1016/j.iot.2025.101531

Saeid Jamshidi , Amin Nikanjam , Kawser Wazed Nafi , Foutse Khomh , Rasoul Rasta

{"title":"Application of deep reinforcement learning for intrusion detection in Internet of Things: A systematic review","authors":"Saeid Jamshidi , Amin Nikanjam , Kawser Wazed Nafi , Foutse Khomh , 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}

引用次数: 0

SA-IDS: A single attribute intrusion detection system for Slow DoS attacks in IoT networks

IF 6 3区计算机科学

Internet of Things Pub Date : 2025-02-07 DOI: 10.1016/j.iot.2025.101512

Andy Reed, Laurence Dooley, Soraya Kouadri Mostefaoui

{"title":"SA-IDS: A single attribute intrusion detection system for Slow DoS attacks in IoT networks","authors":"Andy Reed, Laurence Dooley, Soraya Kouadri Mostefaoui","doi":"10.1016/j.iot.2025.101512","DOIUrl":"10.1016/j.iot.2025.101512","url":null,"abstract":"<div><div>Internet of Things (IoT) technologies are expanding and pervade evermore application domains bringing a raft of positive user benefits. However, the matter of application layer security and the omnipresent danger of Denial of Service (DoS) attacks remains a significant risk to effective IoT performance. DoS is especially serious in IoT networks given the propensity for malicious nodes to mimic legitimate nodes encountering slow connectivity, a problem intensified in very stochastic traffic environments where higher node latencies create even stealthier Slow DoS conditions.</div><div>The contribution this paper presents is a flexible <em>single attribute intrusion detection system</em> (SA-IDS) for IoT networks, which employs a novel variable threshold range for just the delta time network attribute, to accurately detect Slow DoS attacks in highly stochastic traffic, while crucially still being able to reliably discriminate malicious from legitimate slow node activity. Experimental results in a live IoT network compellingly demonstrate the superior detection performance of SA-IDS under the stealthiest Slow DoS attack conditions, where genuine nodes with high latency are almost indistinguishable from malicious nodes, thus rendering existing Slow DoS detection methods ineffective that rely solely on static thresholds based on network traffic attribute analysis.</div></div>","PeriodicalId":29968,"journal":{"name":"Internet of Things","volume":"30 ","pages":"Article 101512"},"PeriodicalIF":6.0,"publicationDate":"2025-02-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143377136","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

An IoT-enabled omnidirectional mobile system for home-based rehabilitation of upper and lower limbs

IF 6 3区计算机科学

Internet of Things Pub Date : 2025-02-06 DOI: 10.1016/j.iot.2025.101525

Lian-Wang Lee , Shih-Ting Wang , I-Hsum Li

{"title":"An IoT-enabled omnidirectional mobile system for home-based rehabilitation of upper and lower limbs","authors":"Lian-Wang Lee , Shih-Ting Wang , I-Hsum Li","doi":"10.1016/j.iot.2025.101525","DOIUrl":"10.1016/j.iot.2025.101525","url":null,"abstract":"<div><div>Robotic systems have made significant strides in stroke rehabilitation, evolving from prototypes to clinical applications. However, challenges remain, including limited clinic accessibility, high costs, bulky equipment, and a lack of versatility for upper and lower limb therapy. Integration into home environments also poses difficulties. To address these issues, we introduce OmniRehab, an innovative home-based rehabilitation system tailored for stroke patients with hemiplegia. OmniRehab combines upper and lower limb rehabilitation on a single omnidirectional platform, supporting both passive and active training modes to accommodate different recovery stages. In passive mode, it provides guided assistance, while in active mode, it promotes voluntary muscle control and neuromuscular re-education. The system leverages Internet of Things (IoT) technology for real-time remote supervision, enabling clinicians to monitor progress and adjust training programs dynamically. To enhance engagement, OmniRehab includes virtual rehabilitation games, offering immersive training that motivates patients while helping them master complex tasks. Experimental validation confirms its effectiveness, demonstrating OmniRehab as a cost-efficient, versatile solution for home-based stroke rehabilitation.</div></div>","PeriodicalId":29968,"journal":{"name":"Internet of Things","volume":"30 ","pages":"Article 101525"},"PeriodicalIF":6.0,"publicationDate":"2025-02-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143348010","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

Privacy-aware fall detection and alert management in smart environments using multimodal devices

IF 6 3区计算机科学

Internet of Things Pub Date : 2025-02-06 DOI: 10.1016/j.iot.2025.101526

Marcos Lupión, Vicente González-Ruiz, Juan F. Sanjuan, Pilar M. Ortigosa

{"title":"Privacy-aware fall detection and alert management in smart environments using multimodal devices","authors":"Marcos Lupión, Vicente González-Ruiz, Juan F. Sanjuan, Pilar M. Ortigosa","doi":"10.1016/j.iot.2025.101526","DOIUrl":"10.1016/j.iot.2025.101526","url":null,"abstract":"<div><div>Falls are a leading cause of injury and mortality, especially among the elderly. While camera-based fall detection systems have shown success, they raise significant privacy concerns. Alternatives using wearable sensors or thermal cameras offer comparable accuracy but have yet to be combined for accurate fall detection. Additionally, most research focuses on fall detection without addressing post-fall user’s condition or personalized alerts. This study aims to develop a privacy-aware fall detection system leveraging wearable sensors and thermal cameras. In addition, an alert system integrates devices such as voice assistants and speakers to assess the user’s status after the fall and notify the event. The system improves detection accuracy, addresses privacy concerns, and enhances alert management through personalized responses. We propose an Internet of Things (IoT)-based system integrating all sensors and devices previously mentioned. Edge-based computation enables real-time detection, with Internet connectivity used only for sending alerts in case of a fall. Various machine learning algorithms and sensor sources are evaluated to determine their impact on detection accuracy. Experimental results show that fall detection using a convolutional neural network with thermal images from three viewpoints achieves an F1-score above 0.98. Similarly, traditional machine learning algorithms applied to wearable sensor data showed high performance (0.93 F1-score). Post-processing techniques effectively remove false positives, improving reliability and adoption in real environments. The proposed system ensures high accuracy while addressing privacy concerns. By integrating multimodal devices and edge-based computing, it offers a scalable, real-time solution for smart environments, ensuring timely responses through personalized alerts after falls.</div></div>","PeriodicalId":29968,"journal":{"name":"Internet of Things","volume":"30 ","pages":"Article 101526"},"PeriodicalIF":6.0,"publicationDate":"2025-02-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143222785","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

PD-DRL: Towards privacy-preserving and energy-sustainable UAV crowdsensing

IF 6 3区计算机科学

Internet of Things Pub Date : 2025-02-06 DOI: 10.1016/j.iot.2025.101520

Xiaohui Chen , Kaimin Wei , Jinpeng Chen , Yongdong Wu

{"title":"PD-DRL: Towards privacy-preserving and energy-sustainable UAV crowdsensing","authors":"Xiaohui Chen , Kaimin Wei , Jinpeng Chen , Yongdong Wu","doi":"10.1016/j.iot.2025.101520","DOIUrl":"10.1016/j.iot.2025.101520","url":null,"abstract":"<div><div>Due to the high altitude advantage of unmanned aerial vehicles (UAVs), UAV crowdsensing has been extensively utilized in smart cities and harsh environments. However, UAVs have limited operational duration owing to energy constraints, dramatically diminishing their working efficiency. Moreover, their flight data is recorded and transmitted in unencrypted text, making it vulnerable to privacy breaches. We propose a privacy-preserving dual-model deep reinforcement learning approach (PD-DRL) to end it. It not only adaptively employs contextual knowledge to switch flight modes to enhance UAVs’ working efficiency but also safeguards the confidentiality of sensitive information during model training. PD-DRL consists of privacy-preserving deep reinforcement learning (P-DRL) and dual-model deep reinforcement learning (D-DRL). The former may integrate two distinct policies to switch between data collection and charging modes adaptively, hence optimizing the UAVs’ flight route. The latter can produce synthetic data to replace raw data during model training, thereby protecting the privacy of sensitive information. Ultimately, we conduct security discussions and comprehensive experiments to assess the effectiveness of PD-DRL. The discussion and experimental results demonstrate that PD-DRL surpasses other comparative algorithms, confirming its efficacy and safety.</div></div>","PeriodicalId":29968,"journal":{"name":"Internet of Things","volume":"30 ","pages":"Article 101520"},"PeriodicalIF":6.0,"publicationDate":"2025-02-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143348285","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

A Fuzzy-based System for assessment of relational trust in IoT and social networks

IF 6 3区计算机科学

Internet of Things Pub Date : 2025-02-05 DOI: 10.1016/j.iot.2025.101528

Shunya Higashi , Phudit Ampririt , Ermioni Qafzezi , Makoto Ikeda , Keita Matsuo , Leonard Barolli

{"title":"A Fuzzy-based System for assessment of relational trust in IoT and social networks","authors":"Shunya Higashi , Phudit Ampririt , Ermioni Qafzezi , Makoto Ikeda , Keita Matsuo , Leonard Barolli","doi":"10.1016/j.iot.2025.101528","DOIUrl":"10.1016/j.iot.2025.101528","url":null,"abstract":"<div><div>In modern digital ecosystems, trust plays a critical role in ensuring secure and reliable interactions across various entities, including humans and machines. As technologies such as 5G wireless networks and the Internet of Things (IoT) drive unprecedented complexity in these environments, the demand for flexible and effective trust evaluation systems has grown exponentially. This paper presents a Fuzzy-based System for Assessment of Relational Trust (FSART), which utilizes fuzzy logic to evaluate trust levels between entities. We implemented two models (FSARTM1 and FSARTM2) considering three parameters: Influence (If), Importance (Ip), and Similarity (Sm) for FSARTM1, while for FSARTM2 we considered Reputation (Rp) as an additional parameter. The simulation results indicate that increasing the values of these parameters leads to a corresponding increase in Relational Trust (RT). For FSARTM1, when Ip is 0.9, all RT values exceed 0.5, while in FSARTM2 when If is 0.9 and moderate values of other parameters, RT consistently remains above 0.5. These results suggest that FSARTM2 provides a more accurate assessment by incorporating Rp, making it a more robust tool for trust evaluation in complex digital ecosystems, including social media, collaborative environments, and IoT systems.</div></div>","PeriodicalId":29968,"journal":{"name":"Internet of Things","volume":"30 ","pages":"Article 101528"},"PeriodicalIF":6.0,"publicationDate":"2025-02-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143348284","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

DDoSViT: IoT DDoS attack detection for fortifying firmware Over-The-Air (OTA) updates using vision transformer

IF 6 3区计算机科学

Internet of Things Pub Date : 2025-02-05 DOI: 10.1016/j.iot.2025.101527

Muhammad Ali , Yasir Saleem , Sadaf Hina , Ghalib A. Shah

{"title":"DDoSViT: IoT DDoS attack detection for fortifying firmware Over-The-Air (OTA) updates using vision transformer","authors":"Muhammad Ali , Yasir Saleem , Sadaf Hina , Ghalib A. Shah","doi":"10.1016/j.iot.2025.101527","DOIUrl":"10.1016/j.iot.2025.101527","url":null,"abstract":"<div><div>The widespread adoption of Internet of Things (IoT) devices has introduced numerous vulnerabilities, particularly in firmware over-the-air (OTA) updates. These updates are essential for improving device functionality and addressing security vulnerabilities. However, they have increasingly become the focus of distributed denial of service (DDoS) attacks designed to disrupt the update process. Historically, the infamous Mirai botnet and its variants have exploited IoT vulnerabilities to carry out successful DDoS attacks. In recent years, deep learning models, especially Vision Transformers, have gained significant attention due to their exceptional performance in image classification tasks. To optimize detection and alert mechanisms, this novel study proposes a DDoSViT framework. This Vision Transformer (ViT)-based multi-vector DDoS and DoS attack detection framework converts attack flows into images and trains Vision Transformers on an attack image dataset. To validate the proposed framework, this study extensively reviewed diverse datasets and selected CICIoT2023 and CICIoMT2024 datasets ensuring these contain real-world attack scenarios and multi-vector real attacks. The proposed methodology and rigorous experimentation demonstrated 99.50% accuracy in multi-class classification across 23 different variants of DDoS and DoS attacks, outperforming contemporary models. The model’s performance was assessed using metrics such as accuracy, precision, recall, and F1-score. This research provides significant benefits to security practitioners and administrators, offering reduced false positives and reliable alerts during firmware over-the-air updates in IoT-edge devices.</div></div>","PeriodicalId":29968,"journal":{"name":"Internet of Things","volume":"30 ","pages":"Article 101527"},"PeriodicalIF":6.0,"publicationDate":"2025-02-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143377135","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