Cluster Head Selection Based on Type-II Fuzzy Logic System in Wireless Sensor Networks: A Review

Abstract

Sensor nodes (SNs) in a Wireless sensor network (WSN) sense physical characteristics such as temperature, light, humidity, and forward the data to a base station (BS). Because of the limited power supply of the SNs, WSN becomes an energy constraint network. So, to extend the network's lifetime, it is required to build energy-effectual protocols. Clustering is an important mechanism that offers an effective way to lower the consumption of energy. However, several soft computing techniques are used for cluster head (CH) selection. Due to the dynamic nature of the environment and random deployment of the SNs, the Type-I fuzzy logic system (FLS) is used that deals with uncertainties of the WSNs and provides accurate results compared to the traditional clustering mechanism. Uncertainties of Type-I FLS are improved by Type-II FLS. So, it is widely used in WSNs for cluster head selection which increases the network performance. In this paper, various clustering protocols using Type-II FLS for cluster head selection are reviewed based upon their properties like the energy of node, node density, node centrality and the distance between the nodes, historical contribution as a CH, efficiency, link quality, and moving speed of the nodes, which helps to attain maximum network lifetime, minimum energy consumption, higher packet delivery ratio, secure data transmission, maximum throughput, and scalability. Clustering protocols are described with their simulation results and compared in terms of their fuzzy input parameters, simulation tools used, merits, and demerits.