Nowadays, the convergence of Mobile Edge Computing (MEC) and vehicular networks has emerged as a vital facilitator for the ever-increasing intelligent onboard applications. This paper proposes a multi-tier task offloading mechanism for MEC-enabled vehicular networks leveraging vehicle-to-everything (V2X) communications. The study focuses on applications with sequential subtasks and explores the collaboration of two tiers. In the vehicle tier, we design a needing vehicle (NV)-helping vehicle (HV) matching scheme and inter-vehicle collaborative computation is studied, with joint optimization of task offloading decision, communication, and computation resource allocation to minimize energy consumption and meet delay requirements. In the roadside unit (RSU) tier, collaboration among RSUs is investigated to further address multi-access issues of subchannel and computation resources for multiple vehicles. A two-step method is designed to first obtain optimal continuous solutions of multifaceted variables, and then derive the solution for discrete uplink subchannel allocation with low complexity. Detailed experiments are conducted to demonstrate the proposed method reduces average energy consumption by at least 15% compared with benchmarks under varying task delay requirements and numbers of vehicles and assess the impact of various parameters on system energy consumption.
Categories: cs.NI eess.SP