Open Access

ARTICLE

Blockchain-Enabled Edge Computing Techniques for Advanced Video Surveillance in Autonomous Vehicles

Mohammad Tabrez Quasim^*, Khair Ul Nisa

Department of Computer Science and Artificial Intelligence, College of Computing and Information Technology, University of Bisha, Bisha, P.O. Box 551, Saudi Arabia

* Corresponding Author: Mohammad Tabrez Quasim. Email:

Computers, Materials & Continua 2025, 83(1), 1239-1255. https://doi.org/10.32604/cmc.2025.061541

Received 27 November 2024; Accepted 12 February 2025; Issue published 26 March 2025

Abstract

The blockchain-based audiovisual transmission systems were built to create a distributed and flexible smart transport system (STS). This system lets customers, video creators, and service providers directly connect with each other. Blockchain-based STS devices need a lot of computer power to change different video feed quality and forms into different versions and structures that meet the needs of different users. On the other hand, existing blockchains can’t support live streaming because they take too long to process and don’t have enough computer power. Large amounts of video data being sent and analyzed put too much stress on networks for vehicles. A video surveillance method is suggested in this paper to improve the performance of the blockchain system’s data and lower the latency across the multiple access edge computing (MEC) system. The integration of MEC and blockchain for video surveillance in autonomous vehicles (IMEC-BVS) framework has been proposed. To deal with this problem, the joint optimization problem is shown using the actor-critical asynchronous advantage (ACAA) method and deep reinforcement training as a Markov Choice Progression (MCP). Simulation results show that the suggested method quickly converges and improves the performance of MEC and blockchain when used together for video surveillance in self-driving cars compared to other methods.

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Keywords

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