Optimization Algorithms for UAV-and-MUV Cooperative Data Collection in Wireless Sensor Networks
The deployment of unmanned aerial vehicles (UAVs) has significantly improved the efficiency of data collection for wireless sensor networks (WSNs). The freshness of collected information from sensors can be measured by the age of information (AoI), which is an important factor to consider in data co...
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-06-01
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| Series: | Drones |
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| Online Access: | https://www.mdpi.com/2504-446X/7/7/408 |
| _version_ | 1797589629922181120 |
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| author | Yu Lu Yi Hong Chuanwen Luo Deying Li Zhibo Chen |
| author_facet | Yu Lu Yi Hong Chuanwen Luo Deying Li Zhibo Chen |
| author_sort | Yu Lu |
| collection | DOAJ |
| description | The deployment of unmanned aerial vehicles (UAVs) has significantly improved the efficiency of data collection for wireless sensor networks (WSNs). The freshness of collected information from sensors can be measured by the age of information (AoI), which is an important factor to consider in data collection. For data collection during long-term mission, the energy limitation of UAVs may cause mission interruption, which makes supplementation of the UAVs’ energy more necessary. To this end, we introduce the mobile unmanned vehicle (MUV) to guarantee the UAVs’ energy supplementation. In this paper, we investigate the problem of multi-UAVs and single-MUV cooperative trajectory planning (MUSM-CTP) for data collection in WSNs with consideration for the AoI the collected data and the limited battery capacity of UAVs. The objective of this problem is to find cooperative flight trajectories for multiple UAVs and to determine the MUV’s travel plan to replace batteries for the UAVs, such that the average AoI of all collected data is minimized. We prove the NP-hardness of the problem and design the algorithm via three phases to solve this: determining candidate hover points based on the affinity propagation (AP) clustering method, constructing the flight trajectories of multiple UAVs based on the genetic algorithm (GA), and designing a travel plan for the MUV. The simulation results verify the effectiveness of the proposed algorithm in improving the freshness of the information collected from all of the sensors. |
| first_indexed | 2024-03-11T01:09:16Z |
| format | Article |
| id | doaj.art-f0b1534ffe19402f99fcbb4041565850 |
| institution | Directory Open Access Journal |
| issn | 2504-446X |
| language | English |
| last_indexed | 2024-03-11T01:09:16Z |
| publishDate | 2023-06-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Drones |
| spelling | doaj.art-f0b1534ffe19402f99fcbb40415658502023-11-18T19:00:37ZengMDPI AGDrones2504-446X2023-06-017740810.3390/drones7070408Optimization Algorithms for UAV-and-MUV Cooperative Data Collection in Wireless Sensor NetworksYu Lu0Yi Hong1Chuanwen Luo2Deying Li3Zhibo Chen4School of Information Science and Technology, Beijing Forestry University, Beijing 100083, ChinaSchool of Information Science and Technology, Beijing Forestry University, Beijing 100083, ChinaSchool of Information Science and Technology, Beijing Forestry University, Beijing 100083, ChinaSchool of Information, Renmin University of China, Beijing 100872, ChinaSchool of Information Science and Technology, Beijing Forestry University, Beijing 100083, ChinaThe deployment of unmanned aerial vehicles (UAVs) has significantly improved the efficiency of data collection for wireless sensor networks (WSNs). The freshness of collected information from sensors can be measured by the age of information (AoI), which is an important factor to consider in data collection. For data collection during long-term mission, the energy limitation of UAVs may cause mission interruption, which makes supplementation of the UAVs’ energy more necessary. To this end, we introduce the mobile unmanned vehicle (MUV) to guarantee the UAVs’ energy supplementation. In this paper, we investigate the problem of multi-UAVs and single-MUV cooperative trajectory planning (MUSM-CTP) for data collection in WSNs with consideration for the AoI the collected data and the limited battery capacity of UAVs. The objective of this problem is to find cooperative flight trajectories for multiple UAVs and to determine the MUV’s travel plan to replace batteries for the UAVs, such that the average AoI of all collected data is minimized. We prove the NP-hardness of the problem and design the algorithm via three phases to solve this: determining candidate hover points based on the affinity propagation (AP) clustering method, constructing the flight trajectories of multiple UAVs based on the genetic algorithm (GA), and designing a travel plan for the MUV. The simulation results verify the effectiveness of the proposed algorithm in improving the freshness of the information collected from all of the sensors.https://www.mdpi.com/2504-446X/7/7/408unmanned aerial vehiclewireless sensor networkage of informationmobile unmanned vehiclecooperative trajectory planning |
| spellingShingle | Yu Lu Yi Hong Chuanwen Luo Deying Li Zhibo Chen Optimization Algorithms for UAV-and-MUV Cooperative Data Collection in Wireless Sensor Networks Drones unmanned aerial vehicle wireless sensor network age of information mobile unmanned vehicle cooperative trajectory planning |
| title | Optimization Algorithms for UAV-and-MUV Cooperative Data Collection in Wireless Sensor Networks |
| title_full | Optimization Algorithms for UAV-and-MUV Cooperative Data Collection in Wireless Sensor Networks |
| title_fullStr | Optimization Algorithms for UAV-and-MUV Cooperative Data Collection in Wireless Sensor Networks |
| title_full_unstemmed | Optimization Algorithms for UAV-and-MUV Cooperative Data Collection in Wireless Sensor Networks |
| title_short | Optimization Algorithms for UAV-and-MUV Cooperative Data Collection in Wireless Sensor Networks |
| title_sort | optimization algorithms for uav and muv cooperative data collection in wireless sensor networks |
| topic | unmanned aerial vehicle wireless sensor network age of information mobile unmanned vehicle cooperative trajectory planning |
| url | https://www.mdpi.com/2504-446X/7/7/408 |
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