Internet of Things最新文献

Mitigating smart contract vulnerabilities in electronic toll collection using blockchain security 利用区块链安全缓解电子收费中的智能合约漏洞

IF 6 3区计算机科学

Internet of Things Pub Date : 2024-11-14 DOI: 10.1016/j.iot.2024.101429

Olfa Ben Rhaiem , Marwa Amara , Radhia Zaghdoud , Lamia Chaari , Maha Metab

{"title":"Mitigating smart contract vulnerabilities in electronic toll collection using blockchain security","authors":"Olfa Ben Rhaiem , Marwa Amara , Radhia Zaghdoud , Lamia Chaari , Maha Metab","doi":"10.1016/j.iot.2024.101429","DOIUrl":"10.1016/j.iot.2024.101429","url":null,"abstract":"<div><div>The Internet of Vehicles (IOV) is a distributed network that provides several services based on vehicle information (e.g., location, speed), such as Electronic Toll Collection (ETC). ETC has been introduced to replace traditional toll booths, where vehicles need to line up to pay, especially during peak travel times. The main advantage of ETC is improved traffic efficiency. However, existing ETC systems often fail to secure the privacy of vehicle information and are vulnerable to fund theft. This makes automatic payments inefficient and susceptible to attacks like the Reentrancy attack.</div><div>In this paper, we leverage the Ethereum blockchain and smart contracts to facilitate automatic payments within the ETC system. The primary challenges addressed include authenticating vehicle data, automatically deducting fees from users’ wallets, and safeguarding against Reentrancy attacks in smart contracts, all while maintaining the confidentiality of distance-related information necessary for fee calculation. To address these concerns, we implement a decentralized application featuring a comprehensive end-to-end verification algorithm that operates at both entry and exit toll points, incorporating robust measures to protect sensitive distance data from potential leaks.</div><div>Results show that the accuracy of fees remains relatively high, with reasonable execution times. Additionally, our system’s gas consumption is more efficient compared to related works, making transactions more cost-effective. These outcomes demonstrate that the proposed system not only secures transactions but also ensures correct and efficient payment services, positioning it as a viable solution for improving the security and functionality of ETC systems.</div></div>","PeriodicalId":29968,"journal":{"name":"Internet of Things","volume":"28 ","pages":"Article 101429"},"PeriodicalIF":6.0,"publicationDate":"2024-11-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142654836","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

LBTMA: An integrated P4-enabled framework for optimized traffic management in SD-IoT networks LBTMA：用于优化 SD-IoT 网络流量管理的 P4 功能集成框架

IF 6 3区计算机科学

Internet of Things Pub Date : 2024-11-13 DOI: 10.1016/j.iot.2024.101432

Ameer El-Sayed , Wael Said , Amr Tolba , Yasser Alginahi , Ahmed A. Toony

{"title":"LBTMA: An integrated P4-enabled framework for optimized traffic management in SD-IoT networks","authors":"Ameer El-Sayed , Wael Said , Amr Tolba , Yasser Alginahi , Ahmed A. Toony","doi":"10.1016/j.iot.2024.101432","DOIUrl":"10.1016/j.iot.2024.101432","url":null,"abstract":"<div><div>This research introduces LBTMA, a novel framework for effective traffic management in Internet of Things (IoT) networks employing software-defined networking (SDN). LBTMA comprises three modules: P4-enabled Stateful Traffic Monitoring (P4-STM), P4-enabled Distributed Load Balancing (P4-DLBS), and P4-enabled Distributed Packet Aggregation and Disaggregation (P4-DPADS). Operating entirely within the data plane, the three modules collaboratively address the challenges of managing high communication traffic from IoT devices. P4-STM utilizes state tables for flow monitoring and anonymization, while introducing a novel multi-controller communication scheme (MCCS) that separates routine data from critical alerts through two dedicated channels. MCCS demonstrated a 25% improvement in throughput and a 51% decrease in latency compared to single controller architecture. P4-DLBS features Enhanced Weighted Round Robin (P4-EWRR) load balancing algorithm, which leverages P4′s distributed decision-making capabilities and inter-switch coordination for enhanced scalability and reduced controller burden. P4-EWRR continuously adjusts server weights based on real-time factors (e.g., queue length, server resource pool, CPU utilization) to ensure efficient resource allocation. In testing, P4-EWRR achieved an average response time of 15 ms and an average packet drop rate of 2%. P4-DPADS employs a hierarchical data plane to efficiently handle high volumes of small IoT packets. It demonstrated an average disaggregation accuracy of 98%, communication overhead reduction rate of 70%, and an impressive average aggregation ratio of 95%. Additionally, P4-DPADS contributes to a 25% reduction in latency and a 40% increase in throughput. The LBTMA framework's modularity and P4 programmability provide flexible, scalable, and efficient traffic management in IoT networks.</div></div>","PeriodicalId":29968,"journal":{"name":"Internet of Things","volume":"28 ","pages":"Article 101432"},"PeriodicalIF":6.0,"publicationDate":"2024-11-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142654837","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

AI-based autonomous UAV swarm system for weed detection and treatment: Enhancing organic orange orchard efficiency with agriculture 5.0 基于人工智能的自主无人机群系统用于杂草检测和处理：利用农业 5.0 提高有机橘园效率

IF 6 3区计算机科学

Internet of Things Pub Date : 2024-11-12 DOI: 10.1016/j.iot.2024.101418

Paula Catala-Roman , Jaume Segura-Garcia , Esther Dura , Enrique A. Navarro-Camba , Jose M. Alcaraz-Calero , Miguel Garcia-Pineda

{"title":"AI-based autonomous UAV swarm system for weed detection and treatment: Enhancing organic orange orchard efficiency with agriculture 5.0","authors":"Paula Catala-Roman , Jaume Segura-Garcia , Esther Dura , Enrique A. Navarro-Camba , Jose M. Alcaraz-Calero , Miguel Garcia-Pineda","doi":"10.1016/j.iot.2024.101418","DOIUrl":"10.1016/j.iot.2024.101418","url":null,"abstract":"<div><div>Weeds significantly threaten agricultural productivity by competing with crops for nutrients, particularly in organic farming, where chemical herbicides are prohibited. On Spain’s Mediterranean coast, organic citrus farms face increasing challenges from invasive species like <em>Araujia sericifera</em> and <em>Cortaderia selloana</em>, which further complicate cover crop management. This study introduces a swarm system of unmanned aerial vehicles (UAVs) equipped with neural networks based on YOLOv10 for the detection and geo-location of these invasive weeds. The system achieves F1-scores of 0.78 for <em>Araujia sericifera</em> and 0.80 for <em>Cortaderia selloana</em>. Using GPS and RTK, the UAVs generate KML files to guide diffuser drones for precise, localized treatments with organic products. By automating the detection, treatment, and elimination of invasive species, the system enhances both productivity and sustainability in organic farming. Additionally, the proposed solution addresses the high labor costs associated with manual weeding by reducing the need for human intervention. A comprehensive economic analysis indicates potential savings ranging from 1810 to 2650 € per hectare, depending on farm size. This innovative approach not only improves weed control efficiency but also promotes environmental sustainability, offering a scalable solution for organic and conventional agriculture alike.</div></div>","PeriodicalId":29968,"journal":{"name":"Internet of Things","volume":"28 ","pages":"Article 101418"},"PeriodicalIF":6.0,"publicationDate":"2024-11-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142654834","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 consortium blockchain-edge enabled authentication scheme for underwater acoustic network (UAN) 用于水下声学网络 (UAN) 的联盟区块链边缘认证方案

IF 6 3区计算机科学

Internet of Things Pub Date : 2024-11-09 DOI: 10.1016/j.iot.2024.101426

Neeraj Kumar, Rifaqat Ali

{"title":"A consortium blockchain-edge enabled authentication scheme for underwater acoustic network (UAN)","authors":"Neeraj Kumar, Rifaqat Ali","doi":"10.1016/j.iot.2024.101426","DOIUrl":"10.1016/j.iot.2024.101426","url":null,"abstract":"<div><div>The Internet of Things (IoT) allows for automated operations in diverse fields, such as agriculture monitoring, pollution monitoring, health care, and underwater monitoring. The Internet of Underwater Things (IoUT) observes the underwater environment, assists in exploration, mitigates disasters, and monitors some factors including temperature, pressure, and pollution. The IoUT relies on a network of intelligent underwater sensors that send data to surface base stations and IoT devices for storage and analysis. Nevertheless, these systems face security risks as they operate in unattended environments. Many authentication methods depend on a centralized third party, leading to higher computation costs and energy usage. To mitigate security risks, autonomous underwater devices need secure connections and authentication. This paper suggests a decentralized authentication mechanism for UAN to safeguard against unauthorized access and ensure secure data storage in the cloud. The proposed mechanism prioritizes robustness, transparency, and energy efficiency. The suggested solution incorporates an architecture based on edge and cloud layers, utilizing customized blockchain technology for secure storage and processing of data. The security of the proposed solution has been thoroughly examined through formal analysis utilizing the Real or Random (ROR) Oracle model and Scyther tool. Informal analysis further confirms the solution’s resilience against various malicious attacks. Additionally, performance and comparative analysis demonstrate that the proposed solution surpasses existing schemes.</div></div>","PeriodicalId":29968,"journal":{"name":"Internet of Things","volume":"28 ","pages":"Article 101426"},"PeriodicalIF":6.0,"publicationDate":"2024-11-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142654833","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

Is artificial intelligence a new battleground for cybersecurity? 人工智能是网络安全的新战场吗？

IF 6 3区计算机科学

Internet of Things Pub Date : 2024-11-06 DOI: 10.1016/j.iot.2024.101428

Khalid Khan , Adnan Khurshid , Javier Cifuentes-Faura

引用次数: 0

Exploring the ALNS method for improved cybersecurity: A deep learning approach for attack detection in IoT and IIoT environments 探索 ALNS 方法，提高网络安全：物联网和 IIoT 环境中攻击检测的深度学习方法

IF 6 3区计算机科学

Internet of Things Pub Date : 2024-11-06 DOI: 10.1016/j.iot.2024.101421

Sarra Cherfi , Ammar Boulaiche , Ali Lemouari

{"title":"Exploring the ALNS method for improved cybersecurity: A deep learning approach for attack detection in IoT and IIoT environments","authors":"Sarra Cherfi , Ammar Boulaiche , Ali Lemouari","doi":"10.1016/j.iot.2024.101421","DOIUrl":"10.1016/j.iot.2024.101421","url":null,"abstract":"<div><div>With the emergence of the Internet of Things (IoT) and the Industrial Internet of Things (IIoT), the flow of data across the world is experiencing a rapid expansion. Unfortunately, this exponential growth is accompanied by a proportional increase in cyber threats, jeopardizing the security and integrity of computer systems. In this context, intrusion detection becomes a necessity to protect networks and systems against potential attacks, ensuring their proper functioning and reliability. In this paper, we propose a deep learning-based model for attack detection. This model utilizes a convolutional neural network to train the datasets, which are first cleaned and preprocessed. The model inputs are selected using an optimization method called adaptive large neighborhood search. The results obtained for the four datasets used – CICIDS2017, Edge-IIoTset, ToN-IoT windows7, and ToN-IoT windows10 – demonstrate the model’s effectiveness for both multi-class and binary classification cases. In the binary case, the accuracy reaches 99.85%, 100%, 99.97%, and 100%, respectively, and in the multi-class case, it stands at 99.81%, 94.98%, 99.92%, and 99.84%, respectively.</div></div>","PeriodicalId":29968,"journal":{"name":"Internet of Things","volume":"28 ","pages":"Article 101421"},"PeriodicalIF":6.0,"publicationDate":"2024-11-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142654832","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

Ensuring patient safety in IoMT: A systematic literature review of behavior-based intrusion detection systems 确保物联网技术中的患者安全：基于行为的入侵检测系统的系统文献综述

IF 6 3区计算机科学

Internet of Things Pub Date : 2024-11-05 DOI: 10.1016/j.iot.2024.101420

Jordi Doménech , Isabel V. Martin-Faus , Saber Mhiri , Josep Pegueroles

{"title":"Ensuring patient safety in IoMT: A systematic literature review of behavior-based intrusion detection systems","authors":"Jordi Doménech , Isabel V. Martin-Faus , Saber Mhiri , Josep Pegueroles","doi":"10.1016/j.iot.2024.101420","DOIUrl":"10.1016/j.iot.2024.101420","url":null,"abstract":"<div><div>Integrating Internet of Medical Things (IoMT) devices into healthcare has enhanced patient care, enabling real-time data exchange and remote monitoring, yet it also presents substantial security risks. Addressing these risks requires robust Intrusion Detection Systems (IDS). While existing studies target this topic, a systematic literature review focused on the current state and advancements in Behavior-based Intrusion Detection Systems for IoMT environments is necessary. This systematic literature review analyzes 81 studies from the past five years, answering three key research questions: (1) What are the Behavior-based IDS currently used in healthcare? (2) How do the detected attacks impact patient safety? (3) Do these IDS include prevention measures? The findings indicate that nearly 84% of the reviewed studies utilize Artificial Intelligence (AI) techniques for threat detection. However, significant challenges persist, such as the scarcity of IoMT-specific datasets, limited focus on patient safety, and the absence of comprehensive prevention and mitigation strategies. This review highlights the need for more robust, patient-centric security solutions. In particular, developing IoMT-specific datasets and enhancing defensive mechanisms are essential to meet the unique security requirements of IoMT environments.</div></div>","PeriodicalId":29968,"journal":{"name":"Internet of Things","volume":"28 ","pages":"Article 101420"},"PeriodicalIF":6.0,"publicationDate":"2024-11-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142654968","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

Reinforcement learning-based drone-assisted collection system for infection samples in IoT environment 基于强化学习的无人机辅助物联网环境感染样本采集系统

IF 6 3区计算机科学

Internet of Things Pub Date : 2024-11-02 DOI: 10.1016/j.iot.2024.101407

Xiuwen Fu , Shengqi Kang

{"title":"Reinforcement learning-based drone-assisted collection system for infection samples in IoT environment","authors":"Xiuwen Fu , Shengqi Kang","doi":"10.1016/j.iot.2024.101407","DOIUrl":"10.1016/j.iot.2024.101407","url":null,"abstract":"<div><div>Since infectious disease surveillance and control rely on efficient sample collection, it is important to research the infection sample collection system. The combination of Internet of Things (IoT) and drone technology provides an emerging solution to this issue. This paper designs a drone-assisted collection system for infection samples (DASS) that provides safe, intelligent, and efficient sample collection services. In this system, flexible collector drones gather infection samples from remote users and return to designated transit points to unload. Meanwhile, deliverer drones shuttle between the testing center and transit points, transporting all packaged infection samples to the testing center. However, the moment when users post collection requests is unknown. This dynamism and uncertainty present new challenges for the routing and scheduling of heterogeneous drones. To address this issue, this paper proposes a deep reinforcement learning-based dynamic sample collection (RLDSC) scheme. Considering the differences in infection samples, minimizing age of samples (AoS) is introduced as an objective. Simulation results indicate that the RLDSC scheme outperforms existing solutions in both effectiveness and efficiency.</div></div>","PeriodicalId":29968,"journal":{"name":"Internet of Things","volume":"28 ","pages":"Article 101407"},"PeriodicalIF":6.0,"publicationDate":"2024-11-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142654969","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

ZFort: A scalable zero-trust approach for trust management and traffic engineering in SDN based IoTs ZFort：基于 SDN 的物联网中信任管理和流量工程的可扩展零信任方法

IF 6 3区计算机科学

Internet of Things Pub Date : 2024-10-29 DOI: 10.1016/j.iot.2024.101419

Usman Ashraf , Mohammed Al-Naeem , Muhammad Nasir Mumtaz Bhutta , Chau Yuen

{"title":"ZFort: A scalable zero-trust approach for trust management and traffic engineering in SDN based IoTs","authors":"Usman Ashraf , Mohammed Al-Naeem , Muhammad Nasir Mumtaz Bhutta , Chau Yuen","doi":"10.1016/j.iot.2024.101419","DOIUrl":"10.1016/j.iot.2024.101419","url":null,"abstract":"<div><div>The Internet of Things (IoT), is a promising solution, but faces critical security challenges in the backdrop of evolving and sophisticated threats. Traditional security models are not well-adopted to protecting these diverse and resource-constrained devices against evolving threats like Advanced Persistent Threats (APTs). We introduce <em>ZFort</em>, a zero-trust framework that prioritizes the security of critical nodes in IoT networks. ZFort dynamically evaluates the risk status of nodes based on node’s criticality and vulnerability scores derived from Common Vulnerabilities and Exposures (CVE) data ZFort dynamically assesses node risk based on criticality and vulnerability scores derived from Common Vulnerabilities and Exposures (CVE) data, and Common Vulnerability Scoring System (CVSS). ZFort uses a stochastic differential equation model for dynamic and continuous trust evaluation between nodes. Based on this evaluation, it dynamically adjusts security measures and routing decisions in real-time. Additionally, ZFort quickly isolates nodes that are likely compromised and prevents routing across them. ZFort uses Mixed Integer Linear Programming (MILP) and efficient heuristics, guaranteeing scalability and resource efficiency even in large networks and enhances the resilience and trustworthiness of key IoT infrastructure.</div></div>","PeriodicalId":29968,"journal":{"name":"Internet of Things","volume":"28 ","pages":"Article 101419"},"PeriodicalIF":6.0,"publicationDate":"2024-10-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142654830","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 3D simulation framework with ray-tracing propagation for LoRaWAN communication 用于LoRaWAN通信的光线跟踪传播三维仿真框架

IF 5.9 3区计算机科学

Internet of Things Pub Date : 2023-10-13 DOI: 10.1016/j.iot.2023.100964

Andres Ruz-Nieto, Esteban Egea-Lopez, Jose-Marıa Molina-Garcıa-Pardo, Jose Santa

{"title":"A 3D simulation framework with ray-tracing propagation for LoRaWAN communication","authors":"Andres Ruz-Nieto, Esteban Egea-Lopez, Jose-Marıa Molina-Garcıa-Pardo, Jose Santa","doi":"10.1016/j.iot.2023.100964","DOIUrl":"https://doi.org/10.1016/j.iot.2023.100964","url":null,"abstract":"<div><p>The adoption of Low-Power Wide-Area Networks (LP-WAN) for interconnecting remote wireless sensors has become a reality in smart scenarios, covering communications needs of large Internet of Things (IoT) deployments. The correct operation and expected performance of such network scenarios, which can range hundreds or thousands of nodes and tens of squared kilometres, should be assessed before carrying out the deployment to save installation and maintenance costs. Common network planning tools can help to roughly study potential coverage, but network simulation offers fine-grained information about network performance. Nevertheless, current simulation frameworks include limited propagation models based on statistical and empirical measurements that do not consider scenario particularities, such as terrain elevation, buildings or vegetation. This is critical in urban settings. In this line, this paper presents a simulation framework including a network simulator, a 3D engine and a ray-tracing tool, which models realistically the performance of Long-Range Wide-Area Network (LoRaWAN) communication technology. We have evaluated the performance of the solution taking as reference experimental campaigns in the city of Cartagena (Spain), comparing data obtained when simulating with the commonly employed propagation models such as Okumura–Hata or path loss. Results indicate that our framework, set-up with data from open geographical information systems, accurately fits experimental values, reporting improvements between 10% and 50% in the error committed when estimating signal strength in challenging urban streets with signal obstruction, as compared with the better performing classical model, Okumura–Hata.</p></div>","PeriodicalId":29968,"journal":{"name":"Internet of Things","volume":"24 ","pages":"Article 100964"},"PeriodicalIF":5.9,"publicationDate":"2023-10-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"50179657","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