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...

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Autores principales:	Soukaina Bouarourou, Abdelhak Boulaalam, El Habib Nfaoui
Formato:	article
Lenguaje:	EN
Publicado:	PeerJ Inc. 2021
Materias:	IoT Sensor Context properties WhaleCLUST TOPSIS Clustering Electronic computers. Computer science QA75.5-76.95
Acceso en línea:	https://doaj.org/article/dee719a823324bf79258da567d24be2e
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spelling	oai:doaj.org-article:dee719a823324bf79258da567d24be2e2021-12-03T15:05:05ZA bio-inspired adaptive model for search and selection in the Internet of Things environment10.7717/peerj-cs.7622376-5992https://doaj.org/article/dee719a823324bf79258da567d24be2e2021-12-01T00:00:00Zhttps://peerj.com/articles/cs-762.pdfhttps://peerj.com/articles/cs-762/https://doaj.org/toc/2376-5992The 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.Soukaina BouarourouAbdelhak BoulaalamEl Habib NfaouiPeerJ Inc.articleIoTSensorContext propertiesWhaleCLUSTTOPSISClusteringElectronic computers. Computer scienceQA75.5-76.95ENPeerJ Computer Science, Vol 7, p e762 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	IoT Sensor Context properties WhaleCLUST TOPSIS Clustering Electronic computers. Computer science QA75.5-76.95
spellingShingle	IoT Sensor Context properties WhaleCLUST TOPSIS Clustering Electronic computers. Computer science QA75.5-76.95 Soukaina Bouarourou Abdelhak Boulaalam El Habib Nfaoui A bio-inspired adaptive model for search and selection in the Internet of Things environment
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	article
author	Soukaina Bouarourou Abdelhak Boulaalam El Habib Nfaoui
author_facet	Soukaina Bouarourou Abdelhak Boulaalam El Habib Nfaoui
author_sort	Soukaina Bouarourou
title	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_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_sort	bio-inspired adaptive model for search and selection in the internet of things environment
publisher	PeerJ Inc.
publishDate	2021
url	https://doaj.org/article/dee719a823324bf79258da567d24be2e
work_keys_str_mv	AT soukainabouarourou abioinspiredadaptivemodelforsearchandselectionintheinternetofthingsenvironment AT abdelhakboulaalam abioinspiredadaptivemodelforsearchandselectionintheinternetofthingsenvironment AT elhabibnfaoui abioinspiredadaptivemodelforsearchandselectionintheinternetofthingsenvironment AT soukainabouarourou bioinspiredadaptivemodelforsearchandselectionintheinternetofthingsenvironment AT abdelhakboulaalam bioinspiredadaptivemodelforsearchandselectionintheinternetofthingsenvironment AT elhabibnfaoui bioinspiredadaptivemodelforsearchandselectionintheinternetofthingsenvironment
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A bio-inspired adaptive model for search and selection in the Internet of Things environment

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