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Energy-Efficient Object Detection and Tracking Framework for Wireless Sensor Network
Object detection and tracking is one of the key applications of wireless sensor networks (WSNs). The key issues associated with this application include network lifetime, object detection and localization accuracy. To ensure the high quality of the service, there should be a trade-off between energy...
Autores principales: | , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2023
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9860737/ https://www.ncbi.nlm.nih.gov/pubmed/36679545 http://dx.doi.org/10.3390/s23020746 |
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author | Dev, Jayashree Mishra, Jibitesh |
author_facet | Dev, Jayashree Mishra, Jibitesh |
author_sort | Dev, Jayashree |
collection | PubMed |
description | Object detection and tracking is one of the key applications of wireless sensor networks (WSNs). The key issues associated with this application include network lifetime, object detection and localization accuracy. To ensure the high quality of the service, there should be a trade-off between energy efficiency and detection accuracy, which is challenging in a resource-constrained WSN. Most researchers have enhanced the application lifetime while achieving target detection accuracy at the cost of high node density. They neither considered the system cost nor the object localization accuracy. Some researchers focused on object detection accuracy while achieving energy efficiency by limiting the detection to a predefined target trajectory. In particular, some researchers only focused on node clustering and node scheduling for energy efficiency. In this study, we proposed a mobile object detection and tracking framework named the Energy Efficient Object Detection and Tracking Framework (EEODTF) for heterogeneous WSNs, which minimizes energy consumption during tracking while not affecting the object detection and localization accuracy. It focuses on achieving energy efficiency via node optimization, mobile node trajectory optimization, node clustering, data reporting optimization and detection optimization. We compared the performance of the EEODTF with the Energy Efficient Tracking and Localization of Object (EETLO) model and the Particle-Swarm-Optimization-based Energy Efficient Target Tracking Model (PSOEETTM). It was found that the EEODTF is more energy efficient than the EETLO and PSOEETTM models. |
format | Online Article Text |
id | pubmed-9860737 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-98607372023-01-22 Energy-Efficient Object Detection and Tracking Framework for Wireless Sensor Network Dev, Jayashree Mishra, Jibitesh Sensors (Basel) Article Object detection and tracking is one of the key applications of wireless sensor networks (WSNs). The key issues associated with this application include network lifetime, object detection and localization accuracy. To ensure the high quality of the service, there should be a trade-off between energy efficiency and detection accuracy, which is challenging in a resource-constrained WSN. Most researchers have enhanced the application lifetime while achieving target detection accuracy at the cost of high node density. They neither considered the system cost nor the object localization accuracy. Some researchers focused on object detection accuracy while achieving energy efficiency by limiting the detection to a predefined target trajectory. In particular, some researchers only focused on node clustering and node scheduling for energy efficiency. In this study, we proposed a mobile object detection and tracking framework named the Energy Efficient Object Detection and Tracking Framework (EEODTF) for heterogeneous WSNs, which minimizes energy consumption during tracking while not affecting the object detection and localization accuracy. It focuses on achieving energy efficiency via node optimization, mobile node trajectory optimization, node clustering, data reporting optimization and detection optimization. We compared the performance of the EEODTF with the Energy Efficient Tracking and Localization of Object (EETLO) model and the Particle-Swarm-Optimization-based Energy Efficient Target Tracking Model (PSOEETTM). It was found that the EEODTF is more energy efficient than the EETLO and PSOEETTM models. MDPI 2023-01-09 /pmc/articles/PMC9860737/ /pubmed/36679545 http://dx.doi.org/10.3390/s23020746 Text en © 2023 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Article Dev, Jayashree Mishra, Jibitesh Energy-Efficient Object Detection and Tracking Framework for Wireless Sensor Network |
title | Energy-Efficient Object Detection and Tracking Framework for Wireless Sensor Network |
title_full | Energy-Efficient Object Detection and Tracking Framework for Wireless Sensor Network |
title_fullStr | Energy-Efficient Object Detection and Tracking Framework for Wireless Sensor Network |
title_full_unstemmed | Energy-Efficient Object Detection and Tracking Framework for Wireless Sensor Network |
title_short | Energy-Efficient Object Detection and Tracking Framework for Wireless Sensor Network |
title_sort | energy-efficient object detection and tracking framework for wireless sensor network |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9860737/ https://www.ncbi.nlm.nih.gov/pubmed/36679545 http://dx.doi.org/10.3390/s23020746 |
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