Intelligent Wireless Sensor Network Sensor Selection and Clustering for Tracking Unmanned Aerial Vehicles
Sensor selection is a vital part of Wireless Sensor Network (WSN) management. This becomes of increased importance when considering the use of low-cost, bearing-only sensor nodes for the tracking of Unmanned Aerial Vehicles (UAVs). However, traditional techniques commonly form excessively large sens...
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MDPI AG
2025-01-01
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| Series: | Sensors |
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| Online Access: | https://www.mdpi.com/1424-8220/25/2/402 |
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| author | Edward-Joseph Cefai Matthew Coombes Daniel O’Boy |
| author_facet | Edward-Joseph Cefai Matthew Coombes Daniel O’Boy |
| author_sort | Edward-Joseph Cefai |
| collection | DOAJ |
| description | Sensor selection is a vital part of Wireless Sensor Network (WSN) management. This becomes of increased importance when considering the use of low-cost, bearing-only sensor nodes for the tracking of Unmanned Aerial Vehicles (UAVs). However, traditional techniques commonly form excessively large sensor clusters, which result in the collection of redundant information, which can deteriorate performance while also increasing the associated network costs. Therefore, this work combines a predictive posterior distribution methodology with a novel simplified objective function for optimally identifying and forming smaller sensor clusters before activation and measurement collection. The goal of the proposed objective function is to reduce network communication and computation costs while still maintaining the tracking performance of using far more sensors. The developed optimisation algorithm results in reducing the size of selected sensor clusters by an average of 50% while still maintaining the tracking performance of general traditional techniques. |
| format | Article |
| id | doaj-art-25bfb8c26bf442adbd7e84528689c5f3 |
| institution | Kabale University |
| issn | 1424-8220 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj-art-25bfb8c26bf442adbd7e84528689c5f32025-01-24T13:48:47ZengMDPI AGSensors1424-82202025-01-0125240210.3390/s25020402Intelligent Wireless Sensor Network Sensor Selection and Clustering for Tracking Unmanned Aerial VehiclesEdward-Joseph Cefai0Matthew Coombes1Daniel O’Boy2Department of Aeronautical and Automotive Engineering, Loughborough University, Loughborough LE11 3TU, UKDepartment of Aeronautical and Automotive Engineering, Loughborough University, Loughborough LE11 3TU, UKDepartment of Aeronautical and Automotive Engineering, Loughborough University, Loughborough LE11 3TU, UKSensor selection is a vital part of Wireless Sensor Network (WSN) management. This becomes of increased importance when considering the use of low-cost, bearing-only sensor nodes for the tracking of Unmanned Aerial Vehicles (UAVs). However, traditional techniques commonly form excessively large sensor clusters, which result in the collection of redundant information, which can deteriorate performance while also increasing the associated network costs. Therefore, this work combines a predictive posterior distribution methodology with a novel simplified objective function for optimally identifying and forming smaller sensor clusters before activation and measurement collection. The goal of the proposed objective function is to reduce network communication and computation costs while still maintaining the tracking performance of using far more sensors. The developed optimisation algorithm results in reducing the size of selected sensor clusters by an average of 50% while still maintaining the tracking performance of general traditional techniques.https://www.mdpi.com/1424-8220/25/2/402Wireless Sensor NetworksUnmanned Aerial Vehiclesensor selectionpredicted posterior distributionsExtended Kalman Filter |
| spellingShingle | Edward-Joseph Cefai Matthew Coombes Daniel O’Boy Intelligent Wireless Sensor Network Sensor Selection and Clustering for Tracking Unmanned Aerial Vehicles Sensors Wireless Sensor Networks Unmanned Aerial Vehicle sensor selection predicted posterior distributions Extended Kalman Filter |
| title | Intelligent Wireless Sensor Network Sensor Selection and Clustering for Tracking Unmanned Aerial Vehicles |
| title_full | Intelligent Wireless Sensor Network Sensor Selection and Clustering for Tracking Unmanned Aerial Vehicles |
| title_fullStr | Intelligent Wireless Sensor Network Sensor Selection and Clustering for Tracking Unmanned Aerial Vehicles |
| title_full_unstemmed | Intelligent Wireless Sensor Network Sensor Selection and Clustering for Tracking Unmanned Aerial Vehicles |
| title_short | Intelligent Wireless Sensor Network Sensor Selection and Clustering for Tracking Unmanned Aerial Vehicles |
| title_sort | intelligent wireless sensor network sensor selection and clustering for tracking unmanned aerial vehicles |
| topic | Wireless Sensor Networks Unmanned Aerial Vehicle sensor selection predicted posterior distributions Extended Kalman Filter |
| url | https://www.mdpi.com/1424-8220/25/2/402 |
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