
A bio-inspired adaptive model for search and selection in the Internet of Things environment
Author(s) -
Soukaina Bouarourou,
Abdelhak Boulaalam,
El Habib Nfaoui
Publication year - 2021
Publication title -
peerj. computer science
Language(s) - English
Resource type - Journals
ISSN - 2376-5992
DOI - 10.7717/peerj-cs.762
Subject(s) - computer science , scalability , cluster analysis , topsis , context (archaeology) , data mining , distributed computing , semantic similarity , matching (statistics) , artificial intelligence , database , paleontology , statistics , mathematics , operations research , engineering , biology
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.