Enhanced Fuzzy Clustering Approach Using Moth flame Optimization Algorithm for Efficient Data Aggregation in Wireless Sensor Networks

Abstract

One of the main challenges we face with wireless sensor networks is how they use energy. Congestion plays a significant role here, as it can shorten the lifespan of WSNs and lead to higher energy consumption by causing data packets to be dropped. In this study, we address these challenges by presenting a distributed fuzzy clustering system that effectively organizes wireless sensor networks into distinct clusters, utilizing two fuzzy logic controllers. We also consider several mobile sink nodes in our approach and provide an additional FLC for selecting fuzzy sinks for the cluster heads. To help reduce energy consumption in WSNs, CHS works together on multi-hop data packet routing. While congestion can still occur in the data forwarding nodes during the routing process, we have a solution. We suggest a distance-based version of the Random Early Detection congestion control technique, which smartly handles the removal of data packets.. We also employ a colony of artificial ants to explore different routing patterns and identify the best routes based on energy usage while utilizing Ant Colony Optimization in WSNs. The ACO algorithm mimics the foraging behavior of ants, with each ant evaluating the energy costs of various routes and using pheromones to highlight the most efficient ones.