Battery-Care Resource Allocation and Task Offloading in Multi-Agent Post-Disaster MEC Environment

被引：0

作者：

Tang, Yiwei ^{[1
,2
]}

Huang, Hualong ^{[1
]}

Zhan, Wenhan ^{[1
]}

Min, Geyong ^{[3
]}

Duan, Zhekai ^{[4
]}

Lei, Yuchuan ^{[1
,5
]}

机构：

[1] Univ Elect Sci & Technol China, Chengdu 611731, Peoples R China

[2] Univ Glasgow, Glasgow G12 8QQ, Scotland

[3] Univ Exeter, Exeter EX4 4QF, England

[4] Univ Edinburgh, Edinburgh EH8 9JU, Scotland

[5] China Telecom Grp Co Ltd, Sichuan Branch, Chengdu 610000, Peoples R China

来源：

2024 IEEE WIRELESS COMMUNICATIONS AND NETWORKING CONFERENCE, WCNC 2024 | 2024年

关键词：

Mobile edge computing; task offloading; resource allocation; battery degradation; multi-agent reinforcement learning;

D O I：

10.1109/WCNC57260.2024.10571159

中图分类号：

TP3 [计算技术、计算机技术];

学科分类号：

0812 ;

摘要：

Being an up-and-coming application scenario of mobile edge computing (MEC), the post-disaster rescue suffers multitudinous computing-intensive tasks but unstably guaranteed network connectivity. In rescue environments, quality of service (QoS), such as task execution delay, energy consumption and battery state of health (SoH), is of significant meaning. This paper studies a multi-user post-disaster MEC environment with unstable 5G communication, where device-to-device (D2D) link communication and dynamic voltage and frequency scaling (DVFS) are adopted to balance each user's requirement for task delay and energy consumption. A battery degradation evaluation approach to prolong battery lifetime is also presented. The distributed optimization problem is formulated into a mixed cooperative-competitive (MCC) multi-agent Markov decision process (MAMDP) and is tackled with recurrent multi-agent Proximal Policy Optimization (rMAPPO). Extensive simulations and comprehensive comparisons with other representative algorithms clearly demonstrate the effectiveness of the proposed rMAPPO-based offloading scheme.

引用

页数：6