UAV deployment in WSN system for emergency/remote area applications

被引:0
|
作者
Hydher, Hassaan [1 ,3 ]
Jayakody, Dushantha Nalin K. [2 ]
Hemachandra, Kasun T. [3 ]
Samarasinghe, Tharaka [3 ]
机构
[1] Sri Lanka Technol Campus, Ctr Telecommun Res, Sch Engn, Padukka 10500, Sri Lanka
[2] Lusofona Univ, COPELABS, P-1749024 Lisbon, Portugal
[3] Univ Moratuwa, Dept Elect & Telecommun Engn, Moratuwa 10400, Sri Lanka
关键词
Inexact line search; Matching algorithm; Modified pattern search; Transmit power fairness; Unmanned aerial vehicles; Wireless sensor networks; WIRELESS SENSOR NETWORKS; DATA-COLLECTION; PLACEMENT; OPTIMIZATION;
D O I
10.1016/j.comnet.2024.110977
中图分类号
TP3 [计算技术、计算机技术];
学科分类号
0812 ;
摘要
Unmanned aerial vehicles (UAVs)-assisted communication systems are considered as a promising technology in diverse verticals. This paper studies the deployment of UAVs in wireless sensor network (WSN) systems. Considering the energy-constrained nature of the wireless sensors, we have proposed a multi-UAV deployment algorithm that minimizes the maximum power transmitted among the sensor nodes (SN) for given minimum data collection rate, minimum data-transferring rate, maximum power and height constraints. The problem is divided into three subproblems in order to reduce the complexity involved in solving them as a single problem. The subproblems are UAV-SN association, 2D positioning of the UAVs and the altitude optimization of the UAVs. Each subproblem is optimized by fixing other parameters as constant. First, the UAV-SN association is addressed using a customized Gale-Shapley algorithm. Second, the 2D positions of the UAVs are optimized using a modified pattern search algorithm. Third, the altitudes of the UAVs are optimized through a customized inexact line search algorithm. Finally, we proposed a combined optimization algorithm that integrates the approaches of all three subproblems in the suitable hierarchy to provide an optimal or a near-optimal solution. In the combined optimization, the first and second subproblems are iteratively solved until the convergence. After that, the third subproblem is solved independently for each UAV. Moreover, the combined optimization gives the minimum number of UAVs required to serve all the SNs with the given rate and power constraints. The numerical simulation validates the efficacy of our proposed algorithms. The results indicate a significant performance gain compared to the benchmark methods in terms of the number of iterations for convergence, maximum transmission power requirement power and the minimum number of UAV requirements.
引用
收藏
页数:14
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