An Incentive Approach for Sustainable Vehicle Resource Utilization in Delay-Energy Sensitive Vehicular Edge Computing
Dun Cao, Shirui Huang, Ning Gu, Pradip Kumar Sharma, Xiaomin Ma, Baofeng Ji · IEEE Transactions on Consumer Electronics · 2024
The rise of artificial intelligence and the Internet of Things (AIoT) has paved the way for the resource utilization in Vehicular Edge Computing (VEC) networks. However, vehicles willingness to participate in collaboration still needs to be investigated due to the high speed dynamics of the network. In this paper, we discuss the case of multiple Task Vehicles (TaVs) competing for resources on multiple Service Vehicles (SeVs) proxied by an RSU. To maximize the utility of both parties, reasonable resource prices and task offloading volume are necessary. Firstly, we formulate the interactions between SeVs and TaVs as a two-stage Stackelberg game. Then, we propose an Optimal Differentiated Pricing Approach (ODPA) to find the optimal solution. It consists of two parts. The first part determines the optimal resource price and task offloading volume by taking into account the energy consumption of SeVs and the delay-energy savings of TaVs. The second part matches SeVs with suitable TaVs to maximize the utility of TaVs and SeVs. Simulation results demonstrate that ODPA increases the overall utility of SeV and TaV by at least 6% and 10%, respectively, reduces the energy consumption and task latency of TaV compared to other benchmark approaches.