Distributed Resource Allocation and Computation Offloading Based on Blockchain in Vehicle Edge Computing
Xiaoting Ruan, Fei Shen, Feng Yan, Lianfeng Shen, Yi Wu, Liang Tang, Yu Zhao, Zhiyong Bu · 2024
Mobile edge computing (MEC) extends cloud computing capabilities to the network edge, enabling efficient processing of compute-intensive tasks from resource-constrained devices. However, static edge servers can lead to service vulnerabilities due to dynamic user device locations. This paper introduces a vehicle edge computing (VEC) architecture, leveraging vehicles’ idle computing resources to serve as mobile edge servers for nearby vehicles. As the number of vehicles and compute-intensive tasks increases, substantial data exchanges between vehicles and edge servers raise significant security risks in VEC networks lacking security measures. To address the security challenges and manage computing resources, we integrate VEC with blockchain technology, creating a secure offloading framework. Focusing on video task offloading generated by vehicle applications, we propose a resource allocation scheme that comprehensively considers video compression, offloading decisions, bandwidth, and computational resource constraints. This scheme is formulated as a mixed, discrete, non-convex joint optimization problem aimed at maximizing system revenue. Since this type of problem is usually an NP-hard problem, discrete variable relaxation and product term replacement are applied to transform it into a convex optimization problem, and an alternating direction method of multipliers-based distributed resource allocation (ADMM-DRA) algorithm is proposed to solve it. Simulation experiments show that compared with other algorithms, ADMM-DRA can achieve the highest system revenue, effectively reduce system latency, and improve resource utilization.