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A Continuous Object Boundary Detection and Tracking Scheme for Failure-Prone Sensor Networks
In wireless sensor networks, detection and tracking of continuous natured objects is more challenging owing to their unique characteristics such as uneven expansion and contraction. A continuous object is usually spread over a large area, and, therefore, a substantial number of sensor nodes are need...
Autores principales: | , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2017
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5335958/ https://www.ncbi.nlm.nih.gov/pubmed/28208815 http://dx.doi.org/10.3390/s17020361 |
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author | Imran, Sajida Ko, Young-Bae |
author_facet | Imran, Sajida Ko, Young-Bae |
author_sort | Imran, Sajida |
collection | PubMed |
description | In wireless sensor networks, detection and tracking of continuous natured objects is more challenging owing to their unique characteristics such as uneven expansion and contraction. A continuous object is usually spread over a large area, and, therefore, a substantial number of sensor nodes are needed to detect the object. Nodes communicate with each other as well as with the sink to exchange control messages and report their detection status. The sink performs computations on the received data to estimate the object boundary. For accurate boundary estimation, nodes at the phenomenon boundary need to be carefully selected. Failure of one or multiple boundary nodes (BNs) can significantly affect the object detection and boundary estimation accuracy at the sink. We develop an efficient failure-prone object detection approach that not only detects and recovers from BN failures but also reduces the number and size of transmissions without compromising the boundary estimation accuracy. The proposed approach utilizes the spatial and temporal features of sensor nodes to detect object BNs. A Voronoi diagram-based network clustering, and failure detection and recovery scheme is used to increase boundary estimation accuracy. Simulation results show the significance of our approach in terms of energy efficiency, communication overhead, and boundary accuracy. |
format | Online Article Text |
id | pubmed-5335958 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2017 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-53359582017-03-16 A Continuous Object Boundary Detection and Tracking Scheme for Failure-Prone Sensor Networks Imran, Sajida Ko, Young-Bae Sensors (Basel) Article In wireless sensor networks, detection and tracking of continuous natured objects is more challenging owing to their unique characteristics such as uneven expansion and contraction. A continuous object is usually spread over a large area, and, therefore, a substantial number of sensor nodes are needed to detect the object. Nodes communicate with each other as well as with the sink to exchange control messages and report their detection status. The sink performs computations on the received data to estimate the object boundary. For accurate boundary estimation, nodes at the phenomenon boundary need to be carefully selected. Failure of one or multiple boundary nodes (BNs) can significantly affect the object detection and boundary estimation accuracy at the sink. We develop an efficient failure-prone object detection approach that not only detects and recovers from BN failures but also reduces the number and size of transmissions without compromising the boundary estimation accuracy. The proposed approach utilizes the spatial and temporal features of sensor nodes to detect object BNs. A Voronoi diagram-based network clustering, and failure detection and recovery scheme is used to increase boundary estimation accuracy. Simulation results show the significance of our approach in terms of energy efficiency, communication overhead, and boundary accuracy. MDPI 2017-02-13 /pmc/articles/PMC5335958/ /pubmed/28208815 http://dx.doi.org/10.3390/s17020361 Text en © 2017 by the authors. 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 (http://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Article Imran, Sajida Ko, Young-Bae A Continuous Object Boundary Detection and Tracking Scheme for Failure-Prone Sensor Networks |
title | A Continuous Object Boundary Detection and Tracking Scheme for Failure-Prone Sensor Networks |
title_full | A Continuous Object Boundary Detection and Tracking Scheme for Failure-Prone Sensor Networks |
title_fullStr | A Continuous Object Boundary Detection and Tracking Scheme for Failure-Prone Sensor Networks |
title_full_unstemmed | A Continuous Object Boundary Detection and Tracking Scheme for Failure-Prone Sensor Networks |
title_short | A Continuous Object Boundary Detection and Tracking Scheme for Failure-Prone Sensor Networks |
title_sort | continuous object boundary detection and tracking scheme for failure-prone sensor networks |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5335958/ https://www.ncbi.nlm.nih.gov/pubmed/28208815 http://dx.doi.org/10.3390/s17020361 |
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