Service Fairness Guarantee Algorithms for Service Caching and Task Offloading of Intelligent and Connected Vehicles Under Information Asymmetry

For delay sensitive tasks in vehicular ad-hoc networks, vehicle to vehicle fog computing can effectively alleviate the heavy burden of computing tasks on roadside units. Existing studies generally assume that roadside units can obtain the global computing capability information of all vehicles in the network, and service vehicles can autonomously provide computation for service requesting vehicles. However, the high control cost to obtain global computing power information and vehicle selfishness have been overlooked. To addressthe vehicle selfishness, information asymmetry and service fairness problem in burden unloading of vehicle fog computing, we propose a service caching and task offloading integer linear programming model, aiming to maximize the minimum system’s service completion rate. By designing an efficient and lightweight incentive mechanism based on contract theory to incentivize vehicles to provide fog computing resources, roadside units do not need to obtain the global vehicle computing capability information, so as to be closer to the real runtime environment. Extensive simulation results demonstrate that the proposed CRA algorithmimproves the minimum service completion rate by approximately 73.16% and 48.72% over the benchmark algorithms, while the decrease in average total throughputs do not exceed 3.39% and 14.96%.