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

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

Research output: Contribution to journalArticlepeer-review

5 Scopus citations

Abstract

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 probability 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.

Original languageEnglish
Pages (from-to)1103-1112
Number of pages10
JournalDigital Communications and Networks
Volume10
Issue number4
DOIs
StatePublished - Aug 2024

Scopus Subject Areas

  • Hardware and Architecture
  • Computer Networks and Communications

Keywords

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

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