Int. J. Metrol. Qual. Eng.
Volume 11, 2020
|Number of page(s)||13|
|Published online||13 November 2020|
Throughput optimization for flying ad hoc network based on position control using genetic algorithm
School of Intelligent Engineering, Zhengzhou University of Aeronautics, Zhengzhou, PR China
2 Department of Technology, Zhengzhou University of Aeronautics, Zhengzhou, PR China
* Corresponding author: firstname.lastname@example.org; email@example.com
Accepted: 23 October 2020
In some complex applications, Flying Ad Hoc Network (FANET) can provide important support for multi UAV (Unmanned Aerial Vehicle) cooperation. In FANET each UAV is equivalent to a router, and the wireless link between them forms a network to achieve the purpose of relay communication. Throughput is an important network performance, and the position of UAV nodes affects it. In this paper, we analyze the influencing factors of FANET throughput with UAV position and terminator selection in first; Secondly we construct the mathematical model of throughput optimization of FANET; Thirdly we propose an algorithm based on genetic algorithm to optimize the position of UAV, and then maximize the throughput. Preparing for using genetic algorithm, we design the related details: Numbering Area, Determining the adjacency matrix and correlation matrix, determining the range of UAV node position movement. The key points of the genetic algorithm for FANET is proposed include the following aspects: coding and population initialization, fitness function, and chromosome replication/crossover/mutation and termination criteria. At last, Matlab is used to simulate the proposed algorithm from three aspects: performance, effect of Radius of Position Constraint (RPC) and effect of Radius of Particle Size (RPS). The results show that the throughput can reach the expected goal by controlling the UAV position, and the optimization speed is related to the RPC and RPS.
Key words: UAV / FANET / throughput / position control / genetic algorithm
© J. Liu et al., published by EDP Sciences, 2020
This is an Open Access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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