Evolutionary Optimization of Drone-Swarm Deployment for Wireless Coverage
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 ove...
| Main Authors: | , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2022-12-01
|
| Series: | Drones |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2504-446X/7/1/8 |
| _version_ | 1827626686749868032 |
|---|---|
| author | Xiao Zhang Xin Xiang Shanshan Lu Yu Zhou Shilong Sun |
| author_facet | Xiao Zhang Xin Xiang Shanshan Lu Yu Zhou Shilong Sun |
| author_sort | Xiao Zhang |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-03-09T13:01:03Z |
| format | Article |
| id | doaj.art-b6721e38a9914b4fbfacbae3d37ae39f |
| institution | Directory Open Access Journal |
| issn | 2504-446X |
| language | English |
| last_indexed | 2024-03-09T13:01:03Z |
| publishDate | 2022-12-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Drones |
| spelling | doaj.art-b6721e38a9914b4fbfacbae3d37ae39f2023-11-30T21:55:00ZengMDPI AGDrones2504-446X2022-12-0171810.3390/drones7010008Evolutionary Optimization of Drone-Swarm Deployment for Wireless CoverageXiao Zhang0Xin Xiang1Shanshan Lu2Yu Zhou3Shilong Sun4College of Computer Science, South-Central Minzu University, Wuhan 430079, ChinaCollege of Computer Science, South-Central Minzu University, Wuhan 430079, ChinaCollege of Computer Science, South-Central Minzu University, Wuhan 430079, ChinaCollege of Computer Science and Software Engineering, Shenzhen University, Shenzhen 518060, ChinaSchool of Mechanical Engineering and Automation, Harbin Institute of Technology, Shenzhen 518055, ChinaThe 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.https://www.mdpi.com/2504-446X/7/1/8drone swarmswireless coverageenergy consumptionmulti-objective optimization |
| spellingShingle | Xiao Zhang Xin Xiang Shanshan Lu Yu Zhou Shilong Sun Evolutionary Optimization of Drone-Swarm Deployment for Wireless Coverage Drones drone swarms wireless coverage energy consumption multi-objective optimization |
| title | Evolutionary Optimization of Drone-Swarm Deployment for Wireless Coverage |
| title_full | Evolutionary Optimization of Drone-Swarm Deployment for Wireless Coverage |
| title_fullStr | Evolutionary Optimization of Drone-Swarm Deployment for Wireless Coverage |
| title_full_unstemmed | Evolutionary Optimization of Drone-Swarm Deployment for Wireless Coverage |
| title_short | Evolutionary Optimization of Drone-Swarm Deployment for Wireless Coverage |
| title_sort | evolutionary optimization of drone swarm deployment for wireless coverage |
| topic | drone swarms wireless coverage energy consumption multi-objective optimization |
| url | https://www.mdpi.com/2504-446X/7/1/8 |
| work_keys_str_mv | AT xiaozhang evolutionaryoptimizationofdroneswarmdeploymentforwirelesscoverage AT xinxiang evolutionaryoptimizationofdroneswarmdeploymentforwirelesscoverage AT shanshanlu evolutionaryoptimizationofdroneswarmdeploymentforwirelesscoverage AT yuzhou evolutionaryoptimizationofdroneswarmdeploymentforwirelesscoverage AT shilongsun evolutionaryoptimizationofdroneswarmdeploymentforwirelesscoverage |