Publication
Optimal Clustering in Wireless Sensor Networks for the Internet of Things Based on Memetic Algorithm
| Summary: | In wireless sensor networks for the Internet of Things (WSN-IoT), the topology deviates very frequently because of the node mobility. The topology maintenance overhead is high in flat-based WSN-IoTs. WSN clustering is suggested to not only reduce the message overhead in WSN-IoT but also control the congestion and easy topology repairs. The partition of wireless mobile nodes (WMNs) into clusters is a multiobjective optimization problem in large-size WSN. Different evolutionary algorithms (EAs) are applied to divide the WSN-IoT into clusters but suffer from early convergence. In this paper, we propose WSN clustering based on the memetic algorithm (MemA) to decrease the probability of early convergence by utilizing local exploration techniques. Optimum clusters in WSN-IoT can be obtained using MemA to dynamically balance the load among clusters. The objective of this research is to find a cluster head set (CH-set) as early as possible once needed. The WMNs with high weight value are selected in lieu of new inhabitants in the subsequent generation. A crossover mechanism is applied to produce new-fangled chromosomes as soon as the two maternities have been nominated. The local search procedure is initiated to enhance the worth of individuals. The suggested method is matched with state-of-the-art methods like MobAC (Singh and Lohani, 2019), EPSO-C (Pathak, 2020), and PBC-CP (Vimalarani, et al. 2016). The proposed technique outperforms the state of the art clustering methods regarding control messages overhead, cluster count, reaffiliation rate, and cluster lifetime. |
|---|---|
| Main Authors: | Ahmad, Masood |
| Other Authors: | Shah, Babar; Ullah, Abrar; Alfandi, Omar; Ali, Gohar; Hameed, Abdul; Moreira, Fernando |
| Subject: | WSN-IoT Wireless sensor networks memeWSN Internet of Things |
| Year: | 2021 |
| Country: | Portugal |
| Document type: | article |
| Access type: | open access |
| Associated institution: | Universidade Portucalense |
| Language: | English |
| Origin: | Repositório da Universidade Portucalense |
| _version_ | 1879709761329430528 |
|---|---|
| author | Ahmad, Masood |
| author2 | Shah, Babar Ullah, Abrar Alfandi, Omar Ali, Gohar Hameed, Abdul Moreira, Fernando |
| author2_role | author author author author author author |
| author_facet | Ahmad, Masood Shah, Babar Ullah, Abrar Alfandi, Omar Ali, Gohar Hameed, Abdul Moreira, Fernando |
| author_role | author |
| country_str | PT |
| creators_json_txt | [{\"Person.name\":\"Ahmad, Masood\"},{\"Person.name\":\"Shah, Babar\"},{\"Person.name\":\"Ullah, Abrar\"},{\"Person.name\":\"Alfandi, Omar\"},{\"Person.name\":\"Ali, Gohar\"},{\"Person.name\":\"Hameed, Abdul\"},{\"Person.name\":\"Moreira, Fernando\"}] |
| datacite.creators.creator.creatorName.fl_str_mv | Ahmad, Masood Shah, Babar Ullah, Abrar Alfandi, Omar Ali, Gohar Hameed, Abdul Moreira, Fernando |
| datacite.date.Accepted.fl_str_mv | 2021-01-06T00:00:00Z |
| datacite.date.available.fl_str_mv | 2021-04-13T10:32:19Z |
| datacite.date.embargoed.fl_str_mv | 2021-04-13T10:32:19Z |
| datacite.rights.fl_str_mv | http://purl.org/coar/access_right/c_abf2 |
| datacite.subjects.subject.fl_str_mv | WSN-IoT Wireless sensor networks memeWSN Internet of Things |
| datacite.titles.title.fl_str_mv | Optimal Clustering in Wireless Sensor Networks for the Internet of Things Based on Memetic Algorithm |
| dc.creator.none.fl_str_mv | Ahmad, Masood Shah, Babar Ullah, Abrar Alfandi, Omar Ali, Gohar Hameed, Abdul Moreira, Fernando |
| dc.date.Accepted.fl_str_mv | 2021-01-06T00:00:00Z |
| dc.date.available.fl_str_mv | 2021-04-13T10:32:19Z |
| dc.date.embargoed.fl_str_mv | 2021-04-13T10:32:19Z |
| dc.format.none.fl_str_mv | application/pdf |
| dc.identifier.none.fl_str_mv | Ahmad, M., Shah, B., Ullah, A., et al. (2021). Optimal Clustering in Wireless Sensor Networks for the Internet of Things Based on Memetic Algorithm. Wireless Communications and Mobile Computing, 2021, (1-14). doi: https://doi.org/10.1155/2021/8875950. Disponível no Repositório UPT, http://hdl.handle.net/11328/3419 http://hdl.handle.net/11328/3419 |
| dc.language.none.fl_str_mv | eng |
| dc.publisher.none.fl_str_mv | Wiley |
| dc.rights.none.fl_str_mv | http://purl.org/coar/access_right/c_abf2 |
| dc.subject.none.fl_str_mv | WSN-IoT Wireless sensor networks memeWSN Internet of Things |
| dc.title.fl_str_mv | Optimal Clustering in Wireless Sensor Networks for the Internet of Things Based on Memetic Algorithm |
| dc.type.none.fl_str_mv | http://purl.org/coar/resource_type/c_6501 |
| description | In wireless sensor networks for the Internet of Things (WSN-IoT), the topology deviates very frequently because of the node mobility. The topology maintenance overhead is high in flat-based WSN-IoTs. WSN clustering is suggested to not only reduce the message overhead in WSN-IoT but also control the congestion and easy topology repairs. The partition of wireless mobile nodes (WMNs) into clusters is a multiobjective optimization problem in large-size WSN. Different evolutionary algorithms (EAs) are applied to divide the WSN-IoT into clusters but suffer from early convergence. In this paper, we propose WSN clustering based on the memetic algorithm (MemA) to decrease the probability of early convergence by utilizing local exploration techniques. Optimum clusters in WSN-IoT can be obtained using MemA to dynamically balance the load among clusters. The objective of this research is to find a cluster head set (CH-set) as early as possible once needed. The WMNs with high weight value are selected in lieu of new inhabitants in the subsequent generation. A crossover mechanism is applied to produce new-fangled chromosomes as soon as the two maternities have been nominated. The local search procedure is initiated to enhance the worth of individuals. The suggested method is matched with state-of-the-art methods like MobAC (Singh and Lohani, 2019), EPSO-C (Pathak, 2020), and PBC-CP (Vimalarani, et al. 2016). The proposed technique outperforms the state of the art clustering methods regarding control messages overhead, cluster count, reaffiliation rate, and cluster lifetime. |
| dirty | 0 |
| eu_rights_str_mv | openAccess |
| format | article |
| id | ruportu_da1a69229b9d88341832ec8fb8be1b38 |
| identifier.url.fl_str_mv | Ahmad, M., Shah, B., Ullah, A., et al. (2021). Optimal Clustering in Wireless Sensor Networks for the Internet of Things Based on Memetic Algorithm. Wireless Communications and Mobile Computing, 2021, (1-14). doi: https://doi.org/10.1155/2021/8875950. Disponível no Repositório UPT, http://hdl.handle.net/11328/3419 http://hdl.handle.net/11328/3419 |
| identifier_str_mv | Ahmad, M., Shah, B., Ullah, A., et al. (2021). Optimal Clustering in Wireless Sensor Networks for the Internet of Things Based on Memetic Algorithm. Wireless Communications and Mobile Computing, 2021, (1-14). doi: https://doi.org/10.1155/2021/8875950. Disponível no Repositório UPT, http://hdl.handle.net/11328/3419 |
| inst_facet_str | urn:organizationAcronym:uportu{{{_:::_}}}Universidade Portucalense |
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| instname_str | Universidade Portucalense |
| language | eng |
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| network_name_str | Repositório da Universidade Portucalense |
| oai_identifier_str | oai:repositorio.upt.pt:11328/3419 |
| organization_str_mv | urn:organizationAcronym:uportu |
| person_str_mv | Ahmad, Masood Shah, Babar Ullah, Abrar Alfandi, Omar Ali, Gohar Hameed, Abdul Moreira, Fernando |
| publishDate | 2021 |
| publisher.none.fl_str_mv | Wiley |
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| spelling | Optimal Clustering in Wireless Sensor Networks for the Internet of Things Based on Memetic AlgorithmAhmad, MasoodShah, BabarUllah, AbrarAlfandi, OmarAli, GoharHameed, AbdulMoreira, FernandoWSN-IoTWireless sensor networksmemeWSNInternet of Thingsopen accesshttp://purl.org/coar/access_right/c_abf2http://creativecommons.org/licenses/by/4.0/Ahmad, M., Shah, B., Ullah, A., et al. (2021). Optimal Clustering in Wireless Sensor Networks for the Internet of Things Based on Memetic Algorithm. Wireless Communications and Mobile Computing, 2021, (1-14). doi: https://doi.org/10.1155/2021/8875950. Disponível no Repositório UPT, http://hdl.handle.net/11328/3419Handlehttp://hdl.handle.net/11328/3419Handle1530-8669 (Print)N/AHasVersion1530-8677 (Online)N/AHasVersionhttps://doi.org/10.1155/2021/8875950DOIHasVersionAhmad, M., Shah, B., Ullah, A., et al. (2021). Optimal Clustering in Wireless Sensor Networks for the Internet of Things Based on Memetic Algorithm. Wireless Communications and Mobile Computing, 2021, (1-14). doi: https://doi.org/10.1155/2021/8875950. Disponível no Repositório UPT, http://hdl.handle.net/11328/3419Handlehttp://hdl.handle.net/11328/3419Handlehttps://doi.org/10.1155/2021/8875950DOI2021-04-13T10:32:19Z2021-04-132021-01-06T00:00:00ZIn wireless sensor networks for the Internet of Things (WSN-IoT), the topology deviates very frequently because of the node mobility. The topology maintenance overhead is high in flat-based WSN-IoTs. WSN clustering is suggested to not only reduce the message overhead in WSN-IoT but also control the congestion and easy topology repairs. The partition of wireless mobile nodes (WMNs) into clusters is a multiobjective optimization problem in large-size WSN. Different evolutionary algorithms (EAs) are applied to divide the WSN-IoT into clusters but suffer from early convergence. In this paper, we propose WSN clustering based on the memetic algorithm (MemA) to decrease the probability of early convergence by utilizing local exploration techniques. Optimum clusters in WSN-IoT can be obtained using MemA to dynamically balance the load among clusters. The objective of this research is to find a cluster head set (CH-set) as early as possible once needed. The WMNs with high weight value are selected in lieu of new inhabitants in the subsequent generation. A crossover mechanism is applied to produce new-fangled chromosomes as soon as the two maternities have been nominated. The local search procedure is initiated to enhance the worth of individuals. The suggested method is matched with state-of-the-art methods like MobAC (Singh and Lohani, 2019), EPSO-C (Pathak, 2020), and PBC-CP (Vimalarani, et al. 2016). The proposed technique outperforms the state of the art clustering methods regarding control messages overhead, cluster count, reaffiliation rate, and cluster lifetime.engapplication/pdfWileyjournal articlehttp://purl.org/coar/resource_type/c_6501literature |
| spellingShingle | Optimal Clustering in Wireless Sensor Networks for the Internet of Things Based on Memetic Algorithm Ahmad, Masood WSN-IoT Wireless sensor networks memeWSN Internet of Things |
| status | SINGLETON |
| subject.fl_str_mv | WSN-IoT Wireless sensor networks memeWSN Internet of Things |
| title | Optimal Clustering in Wireless Sensor Networks for the Internet of Things Based on Memetic Algorithm |
| title_full | Optimal Clustering in Wireless Sensor Networks for the Internet of Things Based on Memetic Algorithm |
| title_fullStr | Optimal Clustering in Wireless Sensor Networks for the Internet of Things Based on Memetic Algorithm |
| title_full_unstemmed | Optimal Clustering in Wireless Sensor Networks for the Internet of Things Based on Memetic Algorithm |
| title_short | Optimal Clustering in Wireless Sensor Networks for the Internet of Things Based on Memetic Algorithm |
| title_sort | Optimal Clustering in Wireless Sensor Networks for the Internet of Things Based on Memetic Algorithm |
| topic | WSN-IoT Wireless sensor networks memeWSN Internet of Things |
| topic_facet | WSN-IoT Wireless sensor networks memeWSN Internet of Things |
| url | http://hdl.handle.net/11328/3419 |
| visible | 1 |
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