Task Offloading and Resource Allocation for MEC-assisted Consumer Internet of Vehicle Systems

TL;DR

This paper proposes an AI-driven framework for task offloading and resource allocation in MEC-assisted Internet of Vehicles, optimizing system delay and energy consumption in dynamic environments.

Contribution

It introduces a multi-MEC architecture and a joint optimization approach using MADDPG for efficient resource management in IoV systems.

Findings

JTOCRA outperforms alternative methods in system performance

The approach demonstrates better scalability in simulations

Optimizes delay and energy consumption effectively

Abstract

Mobile edge computing (MEC)-assisted internet of vehicle (IoV) is emerging as a promising paradigm to provide computing services for vehicles. However, meeting the computing-sensitive and computation-intensive demands of vehicles poses several challenges, including the discrepancy between the limited resource provision and stringent computing requirement, the difficulty in capturing and integrating the intricate features of the MEC-assisted IoV system into the problem formulation, and the need for real-time processing and efficient resource management in the dynamic environment. In this work, we explore the AI-enabled task offloading and resource allocation for MEC-assisted consumer IoV systems. Specifically, we first present a multi-MEC-assisted consumer IoV architecture that leverages the computational resources of MEC servers to provide offloading services close to vehicles. Subsequently, we formulate a system cost minimization optimization problem (SCMOP) by integrating the service delay and energy consumption. To efficiently solve this problem, we design a joint task offloading and computing resource allocation approach (JTOCRA) by applying the multi-agent deep deterministic policy gradient (MADDPG) algorithm. Finally, simulation results demonstrate that the proposed JTOCRA can achieve superior system performances and exhibits better scalability compared to other alternative approaches.