Computation Offloading in MEC-Enabled IoV Networks: Average Energy Efficiency Analysis and Learning-Based Maximization

被引：21
作者：
Ernest, Tan Zheng Hui [1 ]
Madhukumar, A. S. [2 ]
机构：
[1] Agcy Sci Technol & Res, Adv Remfg & Technol Ctr, Singapore 138632, Singapore
[2] Nanyang Technol Univ, Sch Comp Sci & Engn, Singapore 639798, Singapore
来源：
IEEE TRANSACTIONS ON MOBILE COMPUTING | 2024年 / 23卷 / 05期
关键词：
Task analysis; Servers; Resource management; Uplink; Cellular networks; Signal to noise ratio; Interference; Multi-access edge computing; computation offloading; Internet-of-Vehicles; vehicular networks; multi-agent deep reinforcement learning; energy efficiency; OUTAGE-PROBABILITY; RESOURCE-ALLOCATION; POWER ALLOCATION; DEEP; INTERNET; SPECTRUM; ACCESS;
D O I：
10.1109/TMC.2023.3315275
中图分类号：
TP [自动化技术、计算机技术];
学科分类号：
0812 ;
摘要：
This paper investigates the energy efficiency of computation offloading strategies in multi-access edge computing-enabled (MEC-enabled) Internet-of-Vehicles (IoV) networks. First, the energy efficiency of computation offloading strategies in the MEC-enabled IoV network are derived in closed-form. Thereafter, a multi-agent deep reinforcement learning based (MADRL-based) energy efficiency maximization algorithm is proposed to enable computation offloading strategies to attain maximum energy efficiency in the MEC-enabled IoV network. It is shown through extensive analysis that the maximum attained energy efficiency hinges on the choice of task size and transmission timeout threshold, with a computation offloading strategy that jointly considers transmission and computation latencies outperforming existing strategies. It is also shown that the proposed MADRL-based energy efficiency maximization algorithm achieves near-optimal energy efficiency in the MEC-enabled IoV network, making it a promising solution towards achieving energy efficient MEC-enabled IoV networks.
引用
收藏
页码：6074 / 6087
页数：14
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