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SDN-IoT: SDN-based efficient clustering scheme for IoT using improved Sailfish optimization algorithm
The Internet of Things (IoT) includes billions of different devices and various applications that generate a huge amount of data. Due to inherent resource limitations, reliable and robust data transmission for a huge number of heterogenous devices is one of the most critical issues for IoT. Therefor...
Autores principales: | , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
PeerJ Inc.
2023
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10403188/ https://www.ncbi.nlm.nih.gov/pubmed/37547416 http://dx.doi.org/10.7717/peerj-cs.1424 |
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author | Mohammadi, Ramin Akleylek, Sedat Ghaffari, Ali |
author_facet | Mohammadi, Ramin Akleylek, Sedat Ghaffari, Ali |
author_sort | Mohammadi, Ramin |
collection | PubMed |
description | The Internet of Things (IoT) includes billions of different devices and various applications that generate a huge amount of data. Due to inherent resource limitations, reliable and robust data transmission for a huge number of heterogenous devices is one of the most critical issues for IoT. Therefore, cluster-based data transmission is appropriate for IoT applications as it promotes network lifetime and scalability. On the other hand, Software Defined Network (SDN) architecture improves flexibility and makes the IoT respond appropriately to the heterogeneity. This article proposes an SDN-based efficient clustering scheme for IoT using the Improved Sailfish optimization (ISFO) algorithm. In the proposed model, clustering of IoT devices is performed using the ISFO model and the model is installed on the SDN controller to manage the Cluster Head (CH) nodes of IoT devices. The performance evaluation of the proposed model was performed based on two scenarios with 150 and 300 nodes. The results show that for 150 nodes ISFO model in comparison with LEACH, LEACH-E reduced energy consumption by about 21.42% and 17.28%. For 300 ISFO nodes compared to LEACH, LEACH-E reduced energy consumption by about 37.84% and 27.23%. |
format | Online Article Text |
id | pubmed-10403188 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | PeerJ Inc. |
record_format | MEDLINE/PubMed |
spelling | pubmed-104031882023-08-05 SDN-IoT: SDN-based efficient clustering scheme for IoT using improved Sailfish optimization algorithm Mohammadi, Ramin Akleylek, Sedat Ghaffari, Ali PeerJ Comput Sci Artificial Intelligence The Internet of Things (IoT) includes billions of different devices and various applications that generate a huge amount of data. Due to inherent resource limitations, reliable and robust data transmission for a huge number of heterogenous devices is one of the most critical issues for IoT. Therefore, cluster-based data transmission is appropriate for IoT applications as it promotes network lifetime and scalability. On the other hand, Software Defined Network (SDN) architecture improves flexibility and makes the IoT respond appropriately to the heterogeneity. This article proposes an SDN-based efficient clustering scheme for IoT using the Improved Sailfish optimization (ISFO) algorithm. In the proposed model, clustering of IoT devices is performed using the ISFO model and the model is installed on the SDN controller to manage the Cluster Head (CH) nodes of IoT devices. The performance evaluation of the proposed model was performed based on two scenarios with 150 and 300 nodes. The results show that for 150 nodes ISFO model in comparison with LEACH, LEACH-E reduced energy consumption by about 21.42% and 17.28%. For 300 ISFO nodes compared to LEACH, LEACH-E reduced energy consumption by about 37.84% and 27.23%. PeerJ Inc. 2023-07-10 /pmc/articles/PMC10403188/ /pubmed/37547416 http://dx.doi.org/10.7717/peerj-cs.1424 Text en ©2023 Mohammadi 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 | Artificial Intelligence Mohammadi, Ramin Akleylek, Sedat Ghaffari, Ali SDN-IoT: SDN-based efficient clustering scheme for IoT using improved Sailfish optimization algorithm |
title | SDN-IoT: SDN-based efficient clustering scheme for IoT using improved Sailfish optimization algorithm |
title_full | SDN-IoT: SDN-based efficient clustering scheme for IoT using improved Sailfish optimization algorithm |
title_fullStr | SDN-IoT: SDN-based efficient clustering scheme for IoT using improved Sailfish optimization algorithm |
title_full_unstemmed | SDN-IoT: SDN-based efficient clustering scheme for IoT using improved Sailfish optimization algorithm |
title_short | SDN-IoT: SDN-based efficient clustering scheme for IoT using improved Sailfish optimization algorithm |
title_sort | sdn-iot: sdn-based efficient clustering scheme for iot using improved sailfish optimization algorithm |
topic | Artificial Intelligence |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10403188/ https://www.ncbi.nlm.nih.gov/pubmed/37547416 http://dx.doi.org/10.7717/peerj-cs.1424 |
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