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Scheduling optimization for UAV communication coverage using virtual force-based PSO model

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Scheduling optimization for UAV communication coverage using virtual force-based PSO model
When the ground communication base stations in the target area are severely destroyed,the deployment of Unmanned Aerial Vehicle(UAV)ad hoc networks can provide people with temporary communication services.Therefore,it is necessary to design a multi-UAVs cooperative control strategy to achieve better communication coverage and lower energy consumption.In this paper,we propose a multi-UAVs coverage model based on Adaptive Virtual Force-directed Particle Swarm Optimization(AVF-PSO)strategy.In particular,we first introduce the gravity model into the traditional Particle Swarm Optimization(PSO)algorithm so as to increase the prob-ability of full coverage.Then,the energy consumption is included in the calculation of the fitness function so that maximum coverage and energy consumption can be balanced.Finally,in order to reduce the communication interference between UAVs,we design an adaptive lift control strategy based on the repulsion model to reduce the repeated coverage of multi-UAVs.Experimental results show that the proposed coverage strategy based on gravity model outperforms the existing state-of-the-art approaches.For example,in the target area of any size,the coverage rate and the repeated coverage rate of the proposed multi-UAVs scheduling are improved by 6.9-29.1％,and 2.0-56.1％,respectively.Moreover,the proposed scheduling algorithm is high adaptable to diverse execution environments.© 2022 Published by Elsevier Ltd.

Multi-UAVsAd hoc networkArea collaborative coverageGravity modelSwarm optimization algorithmRandom topology

Jianguo Sun、Wenshan Wang、Sizhao Li、Qingan Da、Lei Chen

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College of Computer Science and Technology,Harbin Engineering University,Harbin,Heilongjiang,150001,China

College of Engineering and Computing,Georgia Southern University,Statesboro,GA,30458,USA

Multi-UAVs Ad hoc network Area collaborative coverage Gravity model Swarm optimization algorithm Random topology

2024

10.1016/j.dcan.2023.01.007

数字通信与网络(英文)

数字通信与网络(英文)

ISSN：
年,卷(期)：2024.10(4)