Joint Computation Offloading and Energy Trading in Electric Vehicular Networks

With the rising number of electric vehicles (EVs), the high computational task and energy management of vehicles bring great challenges to the intelligent transportation system. In this work, we investigate the joint offloading and energy trading strategy in vehicular edge computing (VEC) network. We propose an offloading-trading framework, in which EVs can offload tasks to road side unit (RSU) equipped with edge servers or Energy Fog Center (EFC), i.e, edge nodes and fog nodes, and sell excess power to EFC through Vehicle-to-grid (V2G) technology to improve energy efficiency. We aim to maximize the system utility while satisfying the offloading-trading requirements. Since the original problem is non-convex, we decompose it into two subproblems, i.e., trading energy subproblem and trading-offloading subproblem, and proposed the Farthest and Nearest Comparison Searching (FNC-S) algorithm. Specifically, we derive the closed-form expressions of trading electric energy in the trading energy subproblem. Besides, trading-offloading strategy is obtained at two boundaries of distance based on optimal moving distance searching in the trading-offloading subproblem. Simulation results show that the proposed FNC-S algorithm can significantly improve the utility compared with other baseline schemes.