A multi-aerial base station assisted joint computation offloading algorithm based on D3QN in edge VANETs

Abstract

With the rapid development of Internet of Vehicle technology, vehicular edge computing is gradually becoming a key technology for task offloading and resource scheduling. However, the application of traditional base station and vehicle edge network processing introduces large latency, energy consumption and QoS degradation issues for sensitive intensive tasks. To address these issues, we propose a Multi-Aerial Base Station Assisted Joint Computation Offload algorithm based on D3QN in Edge VANETs (MAJVD3). The use of SDN Controllers senses global information and enables efficient resource scheduling. Firstly, a non-convex optimization problem is formulated and proved as an NP-hard problem by combining optimization variables, metrics and environmental constraints. Secondly, we propose to use ϵ-greedy MAJVD3 algorithm for interactive exploration with a dynamic environment to find the optimal solution to non-convex optimization problem. Finally, the network performance, applicability and neural network learning are evaluated separately, and the simulation results shows the effectiveness and convergence of the proposed algorithm. Compared with the existing baseline algorithms, the proposed MAJVD3 algorithm improves the network utility by 4.8%, 100% and 149.6%, respectively. The task latency is reduced by 3%, 30.4% and 30.7%, respectively, and the energy consumption is reduced by 4.8%, 42.3% and 45.7%, respectively.