Energy-Efficient Task Offloading for Vehicular Edge Computing: Joint Optimization of Offloading and Bit Allocation

TL;DR

This paper proposes a joint optimization strategy for task offloading and bit allocation in vehicular edge computing, considering vehicle speed variations to minimize energy consumption under delay constraints.

Contribution

It introduces a novel joint optimization approach that accounts for vehicle speed-induced communication variability, improving energy efficiency in vehicular edge computing.

Findings

Significant reduction in total energy consumption achieved.

Effective offloading strategy adapts to vehicle speed changes.

Optimization outperforms existing methods in simulations.

Abstract

With the rapid development of vehicular networks, various applications that require high computation resources have emerged. To efficiently execute these applications, vehicular edge computing (VEC) can be employed. VEC offloads the computation tasks to the VEC node, i.e., the road side unit (RSU), which improves vehicular service and reduces energy consumption of the vehicle. However, communication environment is time-varying due to the movement of the vehicle, so that finding the optimal offloading parameters is still an open problem. Therefore, it is necessary to investigate an optimal offloading strategy for effective energy savings in energy-limited vehicles. In this paper, we consider the changes of communication environment due to various speeds of vehicles, which are not considered in previous studies. Then, we jointly optimize the offloading proportion and uplink/computation/downlink bit allocation of multiple vehicles, for the purpose of minimizing the total energy consumption of the vehicles under the delay constraint. Numerical results demonstrate that the proposed energy-efficient offloading strategy significantly reduces the total energy consumption.