Resource- and Time-Efficient Computation Offloading in Vehicular Edge Computing: A Max-Min Fairness Oriented Approach

Abstract

Nowadays, computation offloading has become a research focus since it has the potential to solve the challenges faced when dealing with computation-intensive applications in the Internet of Vehicles (IoVs), especially in the 5G or future network environment. However, major issues still exist and the performance of main metrics can be improved to better adapt to the practical scenarios. This paper focuses on achieving resource- and time-efficient computation offloading in IoVs by boosting the cooperation efficiency of vehicles. Firstly, a fuzzy logic-based pricing strategy is designed to evaluate the cooperation tendency and capability of each vehicle from multiple aspects. Vehicles are encouraged to participate in the offloading process even if they are in a disadvantageous position compared to other vehicles. Secondly, a Max-Min fairness-oriented approach is proposed to find the most suitable offloading decision, and vehicles with poor cooperation capabilities are guaranteed to be treated equally in the offloading. Finally, two heuristic algorithms are presented to solve the problem with applicable complexity and to suit the practical IoV environment. Extensive simulation results prove that the proposed approach achieves remarkable performance improvements in terms of delay, service cost and the resource utilization ratios of vehicles.