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

In wireless sensor networks for the Internet of Things (WSN-IoT), topology deviates very frequently because node mobility. The maintenance overhead is high in flat-based WSN-IoTs. WSN clustering suggested to not only reduce message WSN-IoT but also control congestion and easy repairs. partition mobile nodes (WMNs) into clusters a multiobjective optimization problem large-size WSN. Different evolutionary algorithms (EAs) are applied divide suffer from early convergence. this paper, we propose based on memetic algorithm (MemA) decrease probability convergence by utilizing local exploration techniques. Optimum can be obtained using MemA dynamically balance load among clusters. objective research find cluster head set (CH-set) as possible once needed. WMNs with weight value selected lieu new inhabitants subsequent generation. A crossover mechanism produce new-fangled chromosomes soon two maternities have been nominated. search procedure initiated enhance worth individuals. method matched state-of-the-art methods like MobAC (Singh Lohani, 2019), EPSO-C (Pathak, 2020), PBC-CP (Vimalarani, et al. 2016). proposed technique outperforms state art regarding messages overhead, count, reaffiliation rate, lifetime.