Vehicular Edge Cloud Computing: Depressurize the Intelligent Vehicles Onboard Computational Power

TL;DR

This paper proposes a vehicular edge cloud computing framework to offload computational tasks from vehicles to an edge cloud, aiming to improve energy efficiency and meet latency requirements in autonomous and connected vehicles.

Contribution

It introduces a VECC framework with a stochastic fair allocation algorithm for wireless resources, addressing onboard computational limitations and energy consumption issues.

Findings

Enhanced energy efficiency demonstrated through numerical results

Effective wireless resource allocation improves QoS

VECC framework successfully manages vehicular computing demands

Abstract

Recently, with the rapid development of autonomous vehicles and connected vehicles, the demands of vehicular computing keep continuously growing. We notice a constant and limited onboard computational ability can hardly keep up with the rising requirements of the vehicular system and software application during their long-term lifetime, and also at the same time, the vehicles onboard computation causes an increasingly higher vehicular energy consumption. Therefore, we suppose to build a vehicular edge cloud computing (VECC) framework to resolve such a vehicular computing dilemma. In this framework, potential vehicular computing tasks can be executed remotely in an edge cloud within their time latency constraints. Simultaneously, an effective wireless network resources allocation scheme is one of the essential and fundamental factors for the QoS (quality of Service) on the VECC. In this paper, we adopted a stochastic fair allocation (SFA) algorithm to randomly allocate minimum required resource blocks to admitted vehicular users. The numerical results show great effectiveness of energy efficiency in VECC.