Multi-agent resource allocation strategy for UAV swarm-based cooperative sensing

Zhihong WANG; Supeng LENG; Kai XIONG

doi:10.11959/j.issn.2096-3750.2023.00326

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Multi-agent resource allocation strategy for UAV swarm-based cooperative sensing

Topic: Situation-oriented intelligent network and on-demand networking | 更新时间：2024-08-16

- Multi-agent resource allocation strategy for UAV swarm-based cooperative sensing
- Chinese Journal on Internet of Things Vol. 7, Issue 1, Pages: 18-26(2023)
- 作者机构：
  
  1. 电子科技大学信息与通信工程学院，四川成都 611731
  2. 电子科技大学（深圳）高等研究院，广东深圳 518110
- 作者简介：
- 基金信息：
- DOI：10.11959/j.issn.2096-3750.2023.00326
  CLC： TP393
- Online First：2023-03，
  
  Published：30 March 2023
- 稿件说明：
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Zhihong WANG, Supeng LENG, Kai XIONG. Multi-agent resource allocation strategy for UAV swarm-based cooperative sensing[J]. Chinese Journal on Internet of Things, 2023, 7(1): 18-26.
DOI：

Zhihong WANG, Supeng LENG, Kai XIONG. Multi-agent resource allocation strategy for UAV swarm-based cooperative sensing[J]. Chinese Journal on Internet of Things, 2023, 7(1): 18-26. DOI： 10.11959/j.issn.2096-3750.2023.00326.

摘要

在智能物联网技术发展的推动下，无人机集群已广泛用于应急、救援等场景的感知监测。无人机在任务区域自动感知发现任务目标，邻近无人机组成协作感知与协作计算任务群组，协同完成数据的感知、采集和处理。然而，重复的感知资源以及多任务间的计算资源供需不平衡，会造成额外的计算与通信开销，增大端到端处理时延。为了应对这一挑战，提出了一种结合仿生学和多智能体独立强化学习的多任务资源分配策略，基于局部的任务信息进行资源协同分配决策。该方法用任务情景信息浓度表示各个任务的资源需求，并通过情景信息在各任务群组间的扩散，动态更新各任务异构资源需求。同时，结合独立多智能体强化学习方法进行智能决策，以对各任务异构资源进行智能协同分配。仿真结果表明，所提方案不仅能够有效缩短任务执行时间，还可显著提高计算资源利用率。

Abstract

Driven by the development of intelligent internet of things (IoT) technology

unmanned aerial vehicle (UAV) swarms have been widely used for sensing and monitoring in emergency and rescue scenarios.The UAVs automatically sense and discover mission targets in the mission area

recruiting neighboring UAVs to form perception and computation task groups to collaboratively complete the perception

acquisition and processing of data.However

repetitive sensory data and imbalance in the supply and demand of computational resources between multiple tasks cause additional computational and communication overheads and increase the end-to-end processing latency.To address this challenge

a multi-task resource allocation approach combining bionics and multi-agent independent reinforcement learning was proposed

making collaborative resource allocation decisions based on local task information.The method represents the resource requirements of individual tasks as situational information concentrations and dynamically updates the heterogeneous resource requirements of each task by spreading the situational information across task groups.At the same time

it combines multi-agent independent reinforcement learning methods for intelligent decision making in order to collaboratively allocate the heterogeneous resources of each task.Simulation results show that this solution can not only effectively reduce the task execution time

but also significantly improve the computational resource utilization.

关键词

Keywords

references

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