Virtual Force-Based Swarm Trajectory Design for Unmanned Aerial Vehicle-Assisted Data Collection Internet of Things Networks
In this paper, the problem of trajectory design for unmanned aerial vehicle (UAV) swarms in data collection Internet of Things (IoT) networks is studied. In the considered model, the UAV swarm is deployed to patrol a designated area and collect status information from sensors monitoring physical pro...
Saved in:
| Main Authors: | , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2025-01-01
|
| Series: | Drones |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2504-446X/9/1/28 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1832588661786411008 |
|---|---|
| author | Xuanlin Liu Sihua Wang Changchuan Yin |
| author_facet | Xuanlin Liu Sihua Wang Changchuan Yin |
| author_sort | Xuanlin Liu |
| collection | DOAJ |
| description | In this paper, the problem of trajectory design for unmanned aerial vehicle (UAV) swarms in data collection Internet of Things (IoT) networks is studied. In the considered model, the UAV swarm is deployed to patrol a designated area and collect status information from sensors monitoring physical processes. The sense-collect-interchange-explore (SCIE) protocol is proposed to regulate UAV actions, ensuring synchronization and adaptability in a distributed manner. To maintain real-time monitoring while reducing data transmission, we introduce status freshness, which is an extension of age of information (AoI) and allows negative values to reflect the swarm’s predictive capabilities. The trajectory design problem is then formulated as an optimization problem to minimize average status freshness. A virtual force-based algorithm is developed to solve this problem, where UAVs are influenced by attractive forces from sensors and repulsive forces from neighbors. These forces guide UAVs toward sensors requiring data transmission while reducing communication overlap. The proposed distributed algorithm allows each UAV to independently design its trajectory, reducing redundancy and enhancing scalability. Simulation results show that the proposed method can significantly reduce average status freshness under the same energy efficiency conditions compared to artificial potential field algorithm. The proposed method also achieves significantly reduction in terms of communication overhead, compared to fully connected strategies, ensuring scalability in large-scale UAV deployments. |
| format | Article |
| id | doaj-art-e7cb7b360fee484ab04a428b392af882 |
| institution | Kabale University |
| issn | 2504-446X |
| language | English |
| publishDate | 2025-01-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Drones |
| spelling | doaj-art-e7cb7b360fee484ab04a428b392af8822025-01-24T13:29:42ZengMDPI AGDrones2504-446X2025-01-01912810.3390/drones9010028Virtual Force-Based Swarm Trajectory Design for Unmanned Aerial Vehicle-Assisted Data Collection Internet of Things NetworksXuanlin Liu0Sihua Wang1Changchuan Yin2Beijing Laboratory of Advanced Information Network, Beijing University of Posts and Telecommunications, Beijing 100876, ChinaBeijing Laboratory of Advanced Information Network, Beijing University of Posts and Telecommunications, Beijing 100876, ChinaBeijing Laboratory of Advanced Information Network, Beijing University of Posts and Telecommunications, Beijing 100876, ChinaIn this paper, the problem of trajectory design for unmanned aerial vehicle (UAV) swarms in data collection Internet of Things (IoT) networks is studied. In the considered model, the UAV swarm is deployed to patrol a designated area and collect status information from sensors monitoring physical processes. The sense-collect-interchange-explore (SCIE) protocol is proposed to regulate UAV actions, ensuring synchronization and adaptability in a distributed manner. To maintain real-time monitoring while reducing data transmission, we introduce status freshness, which is an extension of age of information (AoI) and allows negative values to reflect the swarm’s predictive capabilities. The trajectory design problem is then formulated as an optimization problem to minimize average status freshness. A virtual force-based algorithm is developed to solve this problem, where UAVs are influenced by attractive forces from sensors and repulsive forces from neighbors. These forces guide UAVs toward sensors requiring data transmission while reducing communication overlap. The proposed distributed algorithm allows each UAV to independently design its trajectory, reducing redundancy and enhancing scalability. Simulation results show that the proposed method can significantly reduce average status freshness under the same energy efficiency conditions compared to artificial potential field algorithm. The proposed method also achieves significantly reduction in terms of communication overhead, compared to fully connected strategies, ensuring scalability in large-scale UAV deployments.https://www.mdpi.com/2504-446X/9/1/28unmanned aerial vehicle (UAV) swarmage of information (AoI)trajectory designvirtual force |
| spellingShingle | Xuanlin Liu Sihua Wang Changchuan Yin Virtual Force-Based Swarm Trajectory Design for Unmanned Aerial Vehicle-Assisted Data Collection Internet of Things Networks Drones unmanned aerial vehicle (UAV) swarm age of information (AoI) trajectory design virtual force |
| title | Virtual Force-Based Swarm Trajectory Design for Unmanned Aerial Vehicle-Assisted Data Collection Internet of Things Networks |
| title_full | Virtual Force-Based Swarm Trajectory Design for Unmanned Aerial Vehicle-Assisted Data Collection Internet of Things Networks |
| title_fullStr | Virtual Force-Based Swarm Trajectory Design for Unmanned Aerial Vehicle-Assisted Data Collection Internet of Things Networks |
| title_full_unstemmed | Virtual Force-Based Swarm Trajectory Design for Unmanned Aerial Vehicle-Assisted Data Collection Internet of Things Networks |
| title_short | Virtual Force-Based Swarm Trajectory Design for Unmanned Aerial Vehicle-Assisted Data Collection Internet of Things Networks |
| title_sort | virtual force based swarm trajectory design for unmanned aerial vehicle assisted data collection internet of things networks |
| topic | unmanned aerial vehicle (UAV) swarm age of information (AoI) trajectory design virtual force |
| url | https://www.mdpi.com/2504-446X/9/1/28 |
| work_keys_str_mv | AT xuanlinliu virtualforcebasedswarmtrajectorydesignforunmannedaerialvehicleassisteddatacollectioninternetofthingsnetworks AT sihuawang virtualforcebasedswarmtrajectorydesignforunmannedaerialvehicleassisteddatacollectioninternetofthingsnetworks AT changchuanyin virtualforcebasedswarmtrajectorydesignforunmannedaerialvehicleassisteddatacollectioninternetofthingsnetworks |