ABSTRACT
Wireless sensor networks (WSNs) play a vital part in Internet of Things (IoT) since these comprise a set of sensor nodes linked via wireless channels that are able to provide digital interface to real-world things. Energy efficiency in WSN plays a crucial role in the functioning of WSN. Clustering in WSN offers a significant energy-efficient technique in WSN. However, it leads to the issue of hot spot problem. For addressing the problem, unequal clustering process in WSN is proposed. In this chapter, a new hybridization of Social Spider with Krill Herd algorithm named SS-KH for unequal clustering in WSN is presented. Here, the SS algorithm initially selects the tentative cluster heads and then the KH algorithm is applied to choose the final cluster heads. The presented SS-KH algorithm effectively selects the cluster heads in an efficient way. The presented algorithm undergoes different scenarios based on the positions of cluster heads. Then, a detailed comparative analysis is made with respect to different measures under several dimensions. The simulation outcome depicted that the proposed model shows maximum network lifetime by lengthening the first node die by 198, 1645, and 2539 rounds, half node die by 174, 1601, and 2460 rounds, and last node die by 160, 1595, and 2502 rounds under three scenarios respectively. These values verified the superior performance of the SS-KH algorithm.
