Task Offloading Method of Internet of Vehicles Based on Cloud-Edge Computing

Abstract

Along with the development of technology and transportation, the internet of vehicles (IoV) has emerged. However, as the number of vehicles on the road increases, the number of computational tasks that need to be processed increases, and thus energy consumption and time latency are a number of relevant factors that we need to take into account in this process. The combination of vehicle-to-everything (V2X) communication technology and mobile edge computing in IoV provides a feasible solution for offloading and processing of computing tasks in vehicles. In this paper, a predictive vehicle task offloading method (PVTO) is proposed to offload computing tasks to the edge server with vehicle-to-vehicle communication (V2V) and vehicle-to- infrastructure communication (V2I), followed by a multi-objective optimization using genetic algorithm, and then a simple additive weighting algorithm (SAW) and a multiple criteria decision making (MCDM) to solve the optimal offloading strategy. Finally, the effectiveness of PVTO is demonstrated by experimental comparison.