Towards Strengthening the Resilience of IoV Networks—A Trust Management Perspective

Wang, Yingxun; Zen, Hushairi; Sabri, Mohd Faizul Bin Mohd; Wang, Xiang; Kho, Lee Chin

doi:10.3390/fi14070202

Cited by 8 publications

(7 citation statements)

References 62 publications

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“…The study investigated how machine learning methods that evaluate fog node trust can enhance resource allocation and system performance. Research [15,16] addressed how accurate demand forecasting for cloud computing resource requirements might lead to improved resource allocation, making sure that fog nodes are ready to manage workload shifts. novel models and hybrid techniques [17].…”

Section: Machine Learning-driven Resource Optimizationmentioning

confidence: 99%

Trust Management and Resource Optimization in Edge and Fog Computing Using the CyberGuard Framework

Alwakeel,

Alnaim

2024

Preprint

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The growing importance of edge and fog computing in the modern IT infrastructure is driven by the rise of decentralized applications. However, resource allocation within these frameworks is challenging due to varying device capabilities and dynamic network conditions. Conventional approaches often result in poor resource use and slowed advancements. This study presents a novel strategy for enhancing resource allocation in edge and fog computing by integrating machine learning with blockchain for reliable trust management. Our proposed framework, called CyberGuard, leverages blockchain’s inherent immutability and decentralization to establish a trustworthy and transparent network for monitoring and verifying edge and fog computing transactions. CyberGuard combines the Trust2Vec model with conventional machine learning models like SVM, KNN, and Random Forests, creating a robust mechanism for assessing trust and security risks. Through detailed optimization and case studies, CyberGuard demonstrates significant improvements in resource allocation efficiency and overall system performance in real-world scenarios. Our results highlight CyberGuard’s effectiveness, evidenced by a remarkable accuracy, precision, recall, and F1-Score of 98.18%, showcasing the transformative potential of our comprehensive approach in edge and fog computing environments.

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Section: Machine Learning-driven Resource Optimizationmentioning

confidence: 99%

Trust Management and Resource Optimization in Edge and Fog Computing Using the CyberGuard Framework

Alwakeel,

Alnaim

2024

Preprint

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“…Furthermore, with the help of advanced analytics techniques, manufacturers can gain insights into the vehicle's behavior and performance, optimize their production processes, and enhance customer experience. The continuous monitoring and remote diagnostic system based on the IoV has the potential to reduce maintenance costs, improve vehicle safety, and enhance overall efficiency [26].…”

Section: Literature Review and Related Workmentioning

confidence: 99%

Smart Automotive Diagnostic and Performance Analysis Using Blockchain Technology

Yassin

Aslan

Halim

2023

JSAN

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The automotive industry currently is seeking to increase remote connectivity to a vehicle, which creates a high demand to implement a secure way of connecting vehicles, as well as verifying and storing their data in a trusted way. Furthermore, much information must be leaked in order to correctly diagnose the vehicle and determine when or how to remotely update it. In this context, we propose a Blockchain-based, fully automated remote vehicle diagnosis system. The proposed system provides a secure and trusted way of storing and verifying vehicle data and analyzing their performance in different environments. Furthermore, we discuss many aspects of the benefits to different parties, such as the vehicle’s owner and manufacturers. Furthermore, a performance evaluation via simulation was performed on the proposed system using MATLAB Simulink to simulate both the vehicles and Blockchain and give a prototype for the system’s structure. In addition, OMNET++ was used to measure the expected system’s storage and throughput given some fixed parameters, such as sending the periodicity and speed. The simulation results showed that the throughput, end-to-end delay, and power consumption increased as the number of vehicles increased. In general, Original Equipment Manufacturers (OEMs) can implement this system by taking into consideration either increasing the storage to add more vehicles or decreasing the sending frequency to allow more vehicles to join. By and large, the proposed system is fully dynamic, and its configuration can be adjusted to satisfy the OEM’s needs since there are no specific constraints while implementing it.

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“…This study aims to mitigate the possible threats and attacks. Moreover, existing studies [10,12,33] discuss the following attacks: on-off attacks, bad-mouthing attacks, good-mouthing attacks, selective misbehavior attacks, time-varying attacks, self-promoting attacks, whitewashing attacks, and newcomer attacks.…”

Section: Security Analysismentioning

confidence: 99%

IoT: A Decentralized Trust Management System Using Blockchain-Empowered Federated Learning

Muazu

Samuel

2022

Sustainability

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We propose a decentralized medical trust management system using blockchain-based federated learning for large-scale Internet of Things (IoT) systems. The proposed system enables health institutions to share data without revealing the privacy of data owners. Health institutions form coalitions and the leader of each coalition is elected based on the proposed proof-of-trust collaboration (PoTC) consensus protocol. The PoTC consensus protocol is based on a weight difference game where trust scores, trust consistency value, and trust deviation are factors used for evaluating nodes in the blockchain. The trust of a node is obtained either through direct trust or recommended trust evaluations. Each leader elects an aggregator who has the most credibility to manage the proposed federated learning system. The leaders become the federated clients as well as validators while the aggregator is the federated server. To ensure the decentralization of nodes, a consortium blockchain is employed. Extensive simulations are performed, which show that the proposed system not only demonstrates scalability and credibility without compromising the accuracy, convergence, and resilience properties against malicious attackers but also outperforms existing trust management systems. A security analysis is also conducted, which shows that the proposed system is robust against trust-related attacks.

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Towards Strengthening the Resilience of IoV Networks—A Trust Management Perspective

Cited by 8 publications

References 62 publications

Trust Management and Resource Optimization in Edge and Fog Computing Using the CyberGuard Framework

Trust Management and Resource Optimization in Edge and Fog Computing Using the CyberGuard Framework

Smart Automotive Diagnostic and Performance Analysis Using Blockchain Technology

IoT: A Decentralized Trust Management System Using Blockchain-Empowered Federated Learning

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