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LinkMind: Link Optimization in Swarming Mobile Sensor Networks
A swarming mobile sensor network is comprised of a swarm of wirelessly connected mobile robots equipped with various sensors. Such a network can be applied in an uncertain environment for services such as cooperative navigation and exploration, object identification and information gathering. One of...
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Formato: | Online Artículo Texto |
Lenguaje: | English |
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Molecular Diversity Preservation International (MDPI)
2011
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Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3231722/ https://www.ncbi.nlm.nih.gov/pubmed/22164070 http://dx.doi.org/10.3390/s110808180 |
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author | Ngo, Trung Dung |
author_facet | Ngo, Trung Dung |
author_sort | Ngo, Trung Dung |
collection | PubMed |
description | A swarming mobile sensor network is comprised of a swarm of wirelessly connected mobile robots equipped with various sensors. Such a network can be applied in an uncertain environment for services such as cooperative navigation and exploration, object identification and information gathering. One of the most advantageous properties of the swarming wireless sensor network is that mobile nodes can work cooperatively to organize an ad-hoc network and optimize the network link capacity to maximize the transmission of gathered data from a source to a target. This paper describes a new method of link optimization of swarming mobile sensor networks. The new method is based on combination of the artificial potential force guaranteeing connectivities of the mobile sensor nodes and the max-flow min-cut theorem of graph theory ensuring optimization of the network link capacity. The developed algorithm is demonstrated and evaluated in simulation. |
format | Online Article Text |
id | pubmed-3231722 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2011 |
publisher | Molecular Diversity Preservation International (MDPI) |
record_format | MEDLINE/PubMed |
spelling | pubmed-32317222011-12-07 LinkMind: Link Optimization in Swarming Mobile Sensor Networks Ngo, Trung Dung Sensors (Basel) Article A swarming mobile sensor network is comprised of a swarm of wirelessly connected mobile robots equipped with various sensors. Such a network can be applied in an uncertain environment for services such as cooperative navigation and exploration, object identification and information gathering. One of the most advantageous properties of the swarming wireless sensor network is that mobile nodes can work cooperatively to organize an ad-hoc network and optimize the network link capacity to maximize the transmission of gathered data from a source to a target. This paper describes a new method of link optimization of swarming mobile sensor networks. The new method is based on combination of the artificial potential force guaranteeing connectivities of the mobile sensor nodes and the max-flow min-cut theorem of graph theory ensuring optimization of the network link capacity. The developed algorithm is demonstrated and evaluated in simulation. Molecular Diversity Preservation International (MDPI) 2011-08-23 /pmc/articles/PMC3231722/ /pubmed/22164070 http://dx.doi.org/10.3390/s110808180 Text en © 2011 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 license (http://creativecommons.org/licenses/by/3.0/). |
spellingShingle | Article Ngo, Trung Dung LinkMind: Link Optimization in Swarming Mobile Sensor Networks |
title | LinkMind: Link Optimization in Swarming Mobile Sensor Networks |
title_full | LinkMind: Link Optimization in Swarming Mobile Sensor Networks |
title_fullStr | LinkMind: Link Optimization in Swarming Mobile Sensor Networks |
title_full_unstemmed | LinkMind: Link Optimization in Swarming Mobile Sensor Networks |
title_short | LinkMind: Link Optimization in Swarming Mobile Sensor Networks |
title_sort | linkmind: link optimization in swarming mobile sensor networks |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3231722/ https://www.ncbi.nlm.nih.gov/pubmed/22164070 http://dx.doi.org/10.3390/s110808180 |
work_keys_str_mv | AT ngotrungdung linkmindlinkoptimizationinswarmingmobilesensornetworks |