Evolutionary Optimization of Drone-Swarm Deployment for Wireless Coverage

被引:5
作者
Zhang, Xiao [1 ]
Xiang, Xin [1 ]
Lu, Shanshan [1 ]
Zhou, Yu [2 ]
Sun, Shilong [3 ,4 ]
机构
[1] South Cent Minzu Univ, Coll Comp Sci, Wuhan 430079, Peoples R China
[2] Shenzhen Univ, Coll Comp Sci & Software Engn, Shenzhen 518060, Peoples R China
[3] Harbin Inst Technol, Sch Mech Engn & Automat, Shenzhen 518055, Peoples R China
[4] Univ Town Shenzhen, HIT Campus, Shenzhen 518055, Peoples R China
基金
中国国家自然科学基金;
关键词
drone swarms; wireless coverage; energy consumption; multi-objective optimization; ALGORITHM; NETWORKS; MOEA/D;
D O I
10.3390/drones7010008
中图分类号
TP7 [遥感技术];
学科分类号
081102 ; 0816 ; 081602 ; 083002 ; 1404 ;
摘要
The need for longer lasting and wider wireless coverage has driven the transition from a single drone to drone swarms. Unlike the single drone, drone swarms can collaboratively achieve full coverage over a target area. However, the existing literature on the drones' wireless coverage has largely overlooked one important fact: that the network lifetime is determined by the minimum leftover energy among all drones. Hence, the maximum energy consumption is minimized in our drone-swarms deployment problem (DSDP), which aims to balance the energy consumption of all drones and maximize the full-coverage network lifetime. We present a genetic algorithm that encodes the solutions as chromosomes and simulates the biological evolution process in search of a favorable solution. Specifically, an integer code scheme is adopted to encode the sequence of the drones' deployment. With the order of the drones' sequence determined by the coding process, we introduce a feasibility checking operator with binary search to improve the performance. By relaxing the constraint of full coverage as an objective of coverage rate, we study the tradeoffs between energy consumption, number of drones, and coverage rate of the target area. By taking advantage of the MOEA/D framework with neighboring subproblems searching, we present a drone-swarms deployment algorithm based on MOEA/D (DSDA-MOEA/D) to find the best tradeoff between these objectives. Extensive simulations were conducted to evaluate the performance of the proposed algorithms.
引用
收藏
页数:19
相关论文
共 37 条
[1]   Enhanced Deployment Strategy for the 5G Drone-BS Using Artificial Intelligence [J].
Al-Turjman, Fadi ;
Lemayian, Joel Poncha ;
Alturjman, Sinem ;
Mostarda, Leonardo .
IEEE ACCESS, 2019, 7 :75999-76008
[2]   Memetic Algorithm-Based Multi-Objective Coverage Optimization for Wireless Sensor Networks [J].
Chen, Zhi ;
Li, Shuai ;
Yue, Wenjing .
SENSORS, 2014, 14 (11) :20500-20518
[3]   Normal-boundary intersection: A new method for generating the Pareto surface in nonlinear multicriteria optimization problems [J].
Das, I ;
Dennis, JE .
SIAM JOURNAL ON OPTIMIZATION, 1998, 8 (03) :631-657
[4]   Multiple Moving Targets Surveillance Based on a Cooperative Network for Multi-UAV [J].
Gu, Jingjing ;
Su, Tao ;
Wang, Qiuhong ;
Du, Xiaojiang ;
Guizani, Mohsen .
IEEE COMMUNICATIONS MAGAZINE, 2018, 56 (04) :82-89
[5]   An Algorithm of Reactive Collision Free 3-D Deployment of Networked Unmanned Aerial Vehicles for Surveillance and Monitoring [J].
Huang, Hailong ;
Savkin, Andrey V. .
IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS, 2020, 16 (01) :132-140
[6]   Modified Distance Calculation in Generational Distance and Inverted Generational Distance [J].
Ishibuchi, Hisao ;
Masuda, Hiroyuki ;
Tanigaki, Yuki ;
Nojima, Yusuke .
EVOLUTIONARY MULTI-CRITERION OPTIMIZATION, PT II, 2015, 9019 :110-125
[7]   A Simple and Fast Hypervolume Indicator-Based Multiobjective Evolutionary Algorithm [J].
Jiang, Siwei ;
Zhang, Jie ;
Ong, Yew-Soon ;
Zhang, Allan N. ;
Tan, Puay Siew .
IEEE TRANSACTIONS ON CYBERNETICS, 2015, 45 (10) :2202-2213
[8]   Task scheduling system for UAV operations in indoor environment [J].
Khosiawan, Yohanes ;
Park, Youngsoo ;
Moon, Ilkyeong ;
Nilakantan, Janardhanan Mukund ;
Nielsen, Izabela .
NEURAL COMPUTING & APPLICATIONS, 2019, 31 (09) :5431-5459
[9]   Multi-objective energy-efficient dense deployment in Wireless Sensor Networks using a hybrid problem-specific MOEA/D [J].
Konstantinidis, Andreas ;
Yang, Kun .
APPLIED SOFT COMPUTING, 2012, 12 (07) :1847-1864
[10]   Multi-objective energy-efficient dense deployment in Wireless Sensor Networks using a hybrid problem-specific MOEA/D [J].
Konstantinidis, Andreas ;
Yang, Kun .
APPLIED SOFT COMPUTING, 2011, 11 (06) :4117-4134