Adaptive MCFL: An adaptive multi-clustering algorithm using fuzzy logic in wireless sensor network

Abstract

In the past, numerous algorithms were tasked with clustering nodes in wireless sensors networks. Each of these algorithms has its own advantages and disadvantages. The common denominator of all these approaches is the constancy of the algorithm in all the rounds of network lifetime that causes the selection of cluster heads in each round. Failing to select the best nodes as cluster heads leads to holding elections in each round. By comparing the chance of each node to be selected as a cluster head using a random number, the majority of these clustering approaches, both fuzzy and non-fuzzy, destroy the chance of selecting the most eligible node as cluster head. As a result, all these approaches require the selection of cluster heads in each round. Selecting cluster heads in each round increases the amount of received and sent messages such that in networks with large number of nodes, it causes some problems such as energy reduction, collision increase, and network traffic. However, by selecting the most eligible nodes as cluster heads and trusting them for at least a few rounds, the amount of sent and received messages is reduced. In this article, an adaptive multiclustering algorithm using fuzzy logic in wireless sensor network (Adaptive MCFL) is presented. In addition to clustering nodes in different rounds using different clustering algorithms, the proposed algorithm avoids selecting new cluster heads by trusting previous cluster heads leading to a reduction in the number of messages and saving energy. The proposed approach is compared with other approaches in three different scenarios using indices such as remaining energy, the number of dead nodes, first node dies (FND), half of nodes die (HND), and last node dies (LND). Results reveal that Adaptive MCFL has as advantage over other approaches.