UAV Enhanced Target-Barrier Coverage Algorithm for Wireless Sensor Networks Based on Reinforcement Learning
Target-barrier coverage is a newly proposed coverage problem in wireless sensor networks (WSNs). The target-barrier is a closed barrier with a distance constraint from the target, which can detect intrusions from outside. In some applications, detecting intrusions from outside and monitoring the tar...
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| Format: | Article |
| Language: | English |
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MDPI AG
2022-08-01
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| Series: | Sensors |
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| Online Access: | https://www.mdpi.com/1424-8220/22/17/6381 |
| _version_ | 1797493270302949376 |
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| author | Li Li Hongbin Chen |
| author_facet | Li Li Hongbin Chen |
| author_sort | Li Li |
| collection | DOAJ |
| description | Target-barrier coverage is a newly proposed coverage problem in wireless sensor networks (WSNs). The target-barrier is a closed barrier with a distance constraint from the target, which can detect intrusions from outside. In some applications, detecting intrusions from outside and monitoring the targets inside the barrier is necessary. However, due to the distance constraint, the target-barrier fails to monitor and detect the target breaching from inside in a timely manner. In this paper, we propose a convex hull attraction (CHA) algorithm to construct the target-barrier and a UAV-enhanced coverage (QUEC) algorithm based on reinforcement learning to cover targets. The CHA algorithm first divides the targets into clusters, then constructs the target-barrier for the outermost targets of the clusters, and the redundant sensors replace the failed sensors. Finally, the UAV’s path is planned based on QUEC. The UAV always covers the target, which is most likely to breach. The simulation results show that, compared with the target-barrier construction algorithm (TBC) and the virtual force algorithm (VFA), CHA can reduce the number of sensors required to construct the target-barrier and extend the target-barrier lifetime. Compared with the traveling salesman problem (TSP), QUEC can reduce the UAV’s coverage completion time, improve the energy efficiency of UAV and the efficiency of detecting targets breaching from inside. |
| first_indexed | 2024-03-10T01:17:36Z |
| format | Article |
| id | doaj.art-f4d5610becfd438ebc314c762374d3c5 |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-03-10T01:17:36Z |
| publishDate | 2022-08-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-f4d5610becfd438ebc314c762374d3c52023-11-23T14:07:28ZengMDPI AGSensors1424-82202022-08-012217638110.3390/s22176381UAV Enhanced Target-Barrier Coverage Algorithm for Wireless Sensor Networks Based on Reinforcement LearningLi Li0Hongbin Chen1School of Information and Communication, Guilin University of Electronic Technology, Guilin 541004, ChinaSchool of Information and Communication, Guilin University of Electronic Technology, Guilin 541004, ChinaTarget-barrier coverage is a newly proposed coverage problem in wireless sensor networks (WSNs). The target-barrier is a closed barrier with a distance constraint from the target, which can detect intrusions from outside. In some applications, detecting intrusions from outside and monitoring the targets inside the barrier is necessary. However, due to the distance constraint, the target-barrier fails to monitor and detect the target breaching from inside in a timely manner. In this paper, we propose a convex hull attraction (CHA) algorithm to construct the target-barrier and a UAV-enhanced coverage (QUEC) algorithm based on reinforcement learning to cover targets. The CHA algorithm first divides the targets into clusters, then constructs the target-barrier for the outermost targets of the clusters, and the redundant sensors replace the failed sensors. Finally, the UAV’s path is planned based on QUEC. The UAV always covers the target, which is most likely to breach. The simulation results show that, compared with the target-barrier construction algorithm (TBC) and the virtual force algorithm (VFA), CHA can reduce the number of sensors required to construct the target-barrier and extend the target-barrier lifetime. Compared with the traveling salesman problem (TSP), QUEC can reduce the UAV’s coverage completion time, improve the energy efficiency of UAV and the efficiency of detecting targets breaching from inside.https://www.mdpi.com/1424-8220/22/17/6381wireless sensor networks (WSNs)target-barrier coverageUnmanned Aerial Vehicle (UAV)trajectory planningreinforcement learning |
| spellingShingle | Li Li Hongbin Chen UAV Enhanced Target-Barrier Coverage Algorithm for Wireless Sensor Networks Based on Reinforcement Learning Sensors wireless sensor networks (WSNs) target-barrier coverage Unmanned Aerial Vehicle (UAV) trajectory planning reinforcement learning |
| title | UAV Enhanced Target-Barrier Coverage Algorithm for Wireless Sensor Networks Based on Reinforcement Learning |
| title_full | UAV Enhanced Target-Barrier Coverage Algorithm for Wireless Sensor Networks Based on Reinforcement Learning |
| title_fullStr | UAV Enhanced Target-Barrier Coverage Algorithm for Wireless Sensor Networks Based on Reinforcement Learning |
| title_full_unstemmed | UAV Enhanced Target-Barrier Coverage Algorithm for Wireless Sensor Networks Based on Reinforcement Learning |
| title_short | UAV Enhanced Target-Barrier Coverage Algorithm for Wireless Sensor Networks Based on Reinforcement Learning |
| title_sort | uav enhanced target barrier coverage algorithm for wireless sensor networks based on reinforcement learning |
| topic | wireless sensor networks (WSNs) target-barrier coverage Unmanned Aerial Vehicle (UAV) trajectory planning reinforcement learning |
| url | https://www.mdpi.com/1424-8220/22/17/6381 |
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