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Nearest Neighbour Node Deployment Algorithm for Mobile Sensor Networks †
Many animal aggregations display remarkable collective coordinated movements on a large scale, which emerge as a result of distributed local decision-making by individuals. The recent advances in modelling the collective motion of animals through the utilisation of Nearest Neighbour rules, without t...
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/PMC10537091/ https://www.ncbi.nlm.nih.gov/pubmed/37765853 http://dx.doi.org/10.3390/s23187797 |
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author | Ghahroudi, Mahsa Sadeghi Shahrabi, Alireza Boutaleb, Tuleen |
author_facet | Ghahroudi, Mahsa Sadeghi Shahrabi, Alireza Boutaleb, Tuleen |
author_sort | Ghahroudi, Mahsa Sadeghi |
collection | PubMed |
description | Many animal aggregations display remarkable collective coordinated movements on a large scale, which emerge as a result of distributed local decision-making by individuals. The recent advances in modelling the collective motion of animals through the utilisation of Nearest Neighbour rules, without the need for centralised coordination, resulted in the development of self-deployment algorithms in Mobile Sensor Networks (MSNs) to achieve various types of coverage essential for different applications. However, the energy consumption associated with sensor movement to achieve the desired coverage remains a significant concern for the majority of algorithms reported in the literature. In this paper, the Nearest Neighbour Node Deployment (NNND) algorithm is proposed to efficiently provide blanket coverage across a given area while minimising energy consumption and enhancing fault tolerance. In contrast to other algorithms that sequentially move sensors, NNND leverages the power of parallelism by employing multiple streams of sensor motions, each directed towards a distinct section of the area. The cohesion of each stream is maintained by adaptively choosing a leader for each stream while collision avoidance is also ensured. These properties contribute to minimising the travel distance within each stream, resulting in decreased energy consumption. Additionally, the utilisation of multiple leaders in NNND eliminates the presence of a single point of failure, hence enhancing the fault tolerance of the area coverage. The results of our extensive simulation study demonstrate that NNND not only achieves lower energy consumption but also a higher percentage of k-coverage. |
format | Online Article Text |
id | pubmed-10537091 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-105370912023-09-29 Nearest Neighbour Node Deployment Algorithm for Mobile Sensor Networks † Ghahroudi, Mahsa Sadeghi Shahrabi, Alireza Boutaleb, Tuleen Sensors (Basel) Article Many animal aggregations display remarkable collective coordinated movements on a large scale, which emerge as a result of distributed local decision-making by individuals. The recent advances in modelling the collective motion of animals through the utilisation of Nearest Neighbour rules, without the need for centralised coordination, resulted in the development of self-deployment algorithms in Mobile Sensor Networks (MSNs) to achieve various types of coverage essential for different applications. However, the energy consumption associated with sensor movement to achieve the desired coverage remains a significant concern for the majority of algorithms reported in the literature. In this paper, the Nearest Neighbour Node Deployment (NNND) algorithm is proposed to efficiently provide blanket coverage across a given area while minimising energy consumption and enhancing fault tolerance. In contrast to other algorithms that sequentially move sensors, NNND leverages the power of parallelism by employing multiple streams of sensor motions, each directed towards a distinct section of the area. The cohesion of each stream is maintained by adaptively choosing a leader for each stream while collision avoidance is also ensured. These properties contribute to minimising the travel distance within each stream, resulting in decreased energy consumption. Additionally, the utilisation of multiple leaders in NNND eliminates the presence of a single point of failure, hence enhancing the fault tolerance of the area coverage. The results of our extensive simulation study demonstrate that NNND not only achieves lower energy consumption but also a higher percentage of k-coverage. MDPI 2023-09-11 /pmc/articles/PMC10537091/ /pubmed/37765853 http://dx.doi.org/10.3390/s23187797 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 Ghahroudi, Mahsa Sadeghi Shahrabi, Alireza Boutaleb, Tuleen Nearest Neighbour Node Deployment Algorithm for Mobile Sensor Networks † |
title | Nearest Neighbour Node Deployment Algorithm for Mobile Sensor Networks † |
title_full | Nearest Neighbour Node Deployment Algorithm for Mobile Sensor Networks † |
title_fullStr | Nearest Neighbour Node Deployment Algorithm for Mobile Sensor Networks † |
title_full_unstemmed | Nearest Neighbour Node Deployment Algorithm for Mobile Sensor Networks † |
title_short | Nearest Neighbour Node Deployment Algorithm for Mobile Sensor Networks † |
title_sort | nearest neighbour node deployment algorithm for mobile sensor networks † |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10537091/ https://www.ncbi.nlm.nih.gov/pubmed/37765853 http://dx.doi.org/10.3390/s23187797 |
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