Open Access

ARTICLE

Optimizing System Latency for Blockchain-Encrypted Edge Computing in Internet of Vehicles

Cui Zhang¹, Maoxin Ji², Qiong Wu^2,*, Pingyi Fan³, Qiang Fan⁴

1 School of Internet of Things Engineering, Wuxi Institute of Technology, Wuxi, 214121, China
2 School of Internet of Things Engineering, Jiangnan University, Wuxi, 214122, China
3 Department of Electronic Engineering, Beijing National Research Center for Information Science and Technology, Tsinghua University, Beijing, 100084, China
4 Qualcomm, San Jose, CA 95110, USA

* Corresponding Author: Qiong Wu. Email:

(This article belongs to the Special Issue: Advanced Communication and Networking Technologies for Internet of Things and Internet of Vehicles)

Computers, Materials & Continua 2025, 83(2), 3519-3536. https://doi.org/10.32604/cmc.2025.061292

Received 21 November 2024; Accepted 01 February 2025; Issue published 16 April 2025

Abstract

As Internet of Vehicles (IoV) technology continues to advance, edge computing has become an important tool for assisting vehicles in handling complex tasks. However, the process of offloading tasks to edge servers may expose vehicles to malicious external attacks, resulting in information loss or even tampering, thereby creating serious security vulnerabilities. Blockchain technology can maintain a shared ledger among servers. In the Raft consensus mechanism, as long as more than half of the nodes remain operational, the system will not collapse, effectively maintaining the system’s robustness and security. To protect vehicle information, we propose a security framework that integrates the Raft consensus mechanism from blockchain technology with edge computing. To address the additional latency introduced by blockchain, we derived a theoretical formula for system delay and proposed a convex optimization solution to minimize the system latency, ensuring that the system meets the requirements for low latency and high reliability. Simulation results demonstrate that the optimized data extraction rate significantly reduces system delay, with relatively stable variations in latency. Moreover, the proposed optimization solution based on this model can provide valuable insights for enhancing security and efficiency in future network environments, such as 5G and next-generation smart city systems.

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Keywords

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