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A bio-inspired adaptive model for search and selection in the Internet of Things environment
The Internet of Things (IoT) is a paradigm that can connect an enormous number of intelligent objects, share large amounts of data, and produce new services. However, it is a challenge to select the proper sensors for a given request due to the number of devices in use, the available resources, the...
Autores principales: | , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
PeerJ Inc.
2021
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8670392/ https://www.ncbi.nlm.nih.gov/pubmed/34977346 http://dx.doi.org/10.7717/peerj-cs.762 |
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author | Bouarourou, Soukaina Boulaalam, Abdelhak Nfaoui, El Habib |
author_facet | Bouarourou, Soukaina Boulaalam, Abdelhak Nfaoui, El Habib |
author_sort | Bouarourou, Soukaina |
collection | PubMed |
description | The Internet of Things (IoT) is a paradigm that can connect an enormous number of intelligent objects, share large amounts of data, and produce new services. However, it is a challenge to select the proper sensors for a given request due to the number of devices in use, the available resources, the restrictions on resource utilization, the nature of IoT networks, and the number of similar services. Previous studies have suggested how to best address this challenge, but suffer from low accuracy and high execution times. We propose a new distributed model to efficiently deal with heterogeneous sensors and select accurate ones in a dynamic IoT environment. The model’s server uses and manages multiple gateways to respond to the request requirements. First, sensors were grouped into three semantic categories and several semantic sensor network types in order to define the space of interest. Second, each type’s sensors were clustered using the Whale-based Sensor Clustering (WhaleCLUST) algorithm according to the context properties. Finally, the Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) was improved to search and select the most adequate sensor matching users’ requirements. Experimental results from real data sets demonstrate that our proposal outperforms state-of-the-art approaches in terms of accuracy (96%), execution time, quality of clustering, and scalability of clustering. |
format | Online Article Text |
id | pubmed-8670392 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | PeerJ Inc. |
record_format | MEDLINE/PubMed |
spelling | pubmed-86703922021-12-30 A bio-inspired adaptive model for search and selection in the Internet of Things environment Bouarourou, Soukaina Boulaalam, Abdelhak Nfaoui, El Habib PeerJ Comput Sci Algorithms and Analysis of Algorithms The Internet of Things (IoT) is a paradigm that can connect an enormous number of intelligent objects, share large amounts of data, and produce new services. However, it is a challenge to select the proper sensors for a given request due to the number of devices in use, the available resources, the restrictions on resource utilization, the nature of IoT networks, and the number of similar services. Previous studies have suggested how to best address this challenge, but suffer from low accuracy and high execution times. We propose a new distributed model to efficiently deal with heterogeneous sensors and select accurate ones in a dynamic IoT environment. The model’s server uses and manages multiple gateways to respond to the request requirements. First, sensors were grouped into three semantic categories and several semantic sensor network types in order to define the space of interest. Second, each type’s sensors were clustered using the Whale-based Sensor Clustering (WhaleCLUST) algorithm according to the context properties. Finally, the Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) was improved to search and select the most adequate sensor matching users’ requirements. Experimental results from real data sets demonstrate that our proposal outperforms state-of-the-art approaches in terms of accuracy (96%), execution time, quality of clustering, and scalability of clustering. PeerJ Inc. 2021-12-01 /pmc/articles/PMC8670392/ /pubmed/34977346 http://dx.doi.org/10.7717/peerj-cs.762 Text en © 2021 Bouarourou et al. https://creativecommons.org/licenses/by/4.0/This is an open access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, distribution, reproduction and adaptation in any medium and for any purpose provided that it is properly attributed. For attribution, the original author(s), title, publication source (PeerJ Computer Science) and either DOI or URL of the article must be cited. |
spellingShingle | Algorithms and Analysis of Algorithms Bouarourou, Soukaina Boulaalam, Abdelhak Nfaoui, El Habib A bio-inspired adaptive model for search and selection in the Internet of Things environment |
title | A bio-inspired adaptive model for search and selection in the Internet of Things environment |
title_full | A bio-inspired adaptive model for search and selection in the Internet of Things environment |
title_fullStr | A bio-inspired adaptive model for search and selection in the Internet of Things environment |
title_full_unstemmed | A bio-inspired adaptive model for search and selection in the Internet of Things environment |
title_short | A bio-inspired adaptive model for search and selection in the Internet of Things environment |
title_sort | bio-inspired adaptive model for search and selection in the internet of things environment |
topic | Algorithms and Analysis of Algorithms |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8670392/ https://www.ncbi.nlm.nih.gov/pubmed/34977346 http://dx.doi.org/10.7717/peerj-cs.762 |
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