Optimal Clustering in Wireless Sensor Networks for the Internet of Things Based on Memetic Algorithm

• Autor(a) principal: Ahmad, Masood
• Data de Publicação: 2021
• Outros Autores: Shah, Babar, Ullah, Abrar, Moreira, Fernando, Alfandi, Omar, Ali, Gohar, Hameed, Abdul
• Tipo de documento: Artigo
• Idioma: eng
• Título da fonte: Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
• Texto Completo: http://hdl.handle.net/11328/3419
• topic: WSN-IoT; Wireless sensor networks; memeWSN; Internet of Things

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.