A Cooperative Resource Optimization Framework for Blockchain-based Vehicular Networks with MEC
Jing Zhang, Fei Shen, Liang Tang, Feng Yan, Fei Qin, Lianfeng Shen · 2023
Video surveillance in intelligent transportation systems is advancing rapidly, with video analytics technology being used to enhance the security of the Internet of Vehicles (IoV) system. However, the sheer volume of video data from cameras and the computational intensity of video analysis pose significant challenges to the IoV network. To address this, mobile edge computing (MEC) has been introduced to offload video tasks from cameras to mobile edge servers/groups formed by vehicles. However, the resource-constrained nature of edge servers and vehicle groups necessitates the design of effective offloading strategies. Additionally, ensuring the security of user data during transmission and computation is a pressing issue. Moreover, the heterogeneous devices in the IoV system may be reluctant to participate in the collaborative processing of video tasks due to mistrust and lack of incentives. To tackle these challenges, we propose a cooperative computing offloading and resource allocation framework that integrates blockchain and MEC to provide secure and low-latency computing offloading services for the IoV system. We also design an efficient incentive mechanism to promote the collaborative processing of video tasks. Our framework formulates computing offloading and resource allocation as a joint optimization problem to maximize the system revenue, and we propose an algorithm based on the alternating direction method of multipliers (ADMM) to solve the distributed optimization problem with fast convergence and low complexity. Simulation results demonstrate that compared to the typical baselines, our scheme can achieve the maximum system revenue and effectively reduce the system delay.