IoV-SDCM: An IoV Secure Data Communication Model Based on Network Encoding and Relay Collaboration

Sun, Yan; Yin, Lihua; Ma, Ying; Wang, Chonghua

doi:10.1155/2022/6546004

Cited by 2 publications

(2 citation statements)

References 27 publications

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“…Y. Sun et al in [36] analyzed major problems and challenges in autonomous vehicles. Bonawitz et al in [37][38][39] analyzed the problems faced by vehicles from the perspective of data management.…”

Section: Related Workmentioning

confidence: 99%

Towards Efficient Federated Learning Using Agile Aggregation in Internet of Vehicles

He,

Hu,

Wang

et al. 2023

Security and Communication Networks

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Federated learning is an enabling technology for the services in Internet of vehicles because it can effectively alleviate privacy issues in data circulation and diversified intelligent applications. However, existing federated learning methods still confront the problem of low computational efficiency when applied to the scenario of high-dynamic vehicles. In order to address this problem, we present a multistep federated learning architecture including task release, vehicle model training, identity authentication, quality assessment, and weighted aggregation processes, where a novel agile aggregation model is established. Based on the agile aggregation model, an efficient federated learning (EFL) algorithm is proposed, which is specifically applied to the scenario of high-dynamic vehicles during the training model. We theoretically analyze its advantages over the existing traditional federated learning algorithms in terms of efficiency, training accuracy, and security. Simulations are conducted using the MNIST dataset to evaluate the performance of the proposed EFL algorithm. The results show that EFL improves the efficiency of the federated training process with effective accuracy and privacy preservation.

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Section: Related Workmentioning

confidence: 99%

Towards Efficient Federated Learning Using Agile Aggregation in Internet of Vehicles

He,

Hu,

Wang

et al. 2023

Security and Communication Networks

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“…Ref. [10] expanded upon the security and privacy considerations in V2V networks as the number of vehicles accessing the network increases and proposed a comprehensive scheme that combines network coding, relay collaboration, and homomorphic encryption. The scheme ensures that the original information remains inaccessible to relay nodes, except for the intended target vehicle node.…”

Section: Introductionmentioning

confidence: 99%

Efficient Communications in V2V Networks with Two-Way Lanes Based on Random Linear Network Coding

Zhang,

Zhu,

2023

Entropy

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Vehicle-to-vehicle (V2V) communication has gained significant attention in the field of intelligent transportation systems. In this paper, we focus on communication scenarios involving vehicles moving in the same and opposite directions. Specifically, we model a V2V network as a dynamic multi-source single-sink network with two-way lanes. To address rapid changes in network topology, we employ random linear network coding (RLNC), which eliminates the need for knowledge of the network topology. We begin by deriving the lower bound for the generation probability. Through simulations, we analyzed the probability distribution and cumulative probability distribution of latency under varying packet loss rates and batch sizes. Our results demonstrated that our RLNC scheme significantly reduced the communication latency, even under challenging channel conditions, when compared to the non-coding case.

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IoV-SDCM: An IoV Secure Data Communication Model Based on Network Encoding and Relay Collaboration

Cited by 2 publications

References 27 publications

Towards Efficient Federated Learning Using Agile Aggregation in Internet of Vehicles

Towards Efficient Federated Learning Using Agile Aggregation in Internet of Vehicles

Efficient Communications in V2V Networks with Two-Way Lanes Based on Random Linear Network Coding

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