Particle Swarm Optimization based Communication and Fault Tolerant Clustering Protocol for Wireless Communication

Abstract

In wireless sensor networks (WSNs), energy efficiency can be achieved through the implementation of a variety of various strategies, the sole purpose of which is to lower overall energy consumption. However, fault tolerance and energy balancing give equal importance for enhancing the network lifetime. A shortest path is what defines for the Base Station (BS), although it is important to have both. Clustering is one of the effective approaches for wireless sensor networks and has been believed to be one of the best ways to save energy in wireless sensor networks. Failures can occur as a result of excessive overload, which causes more energy to be consumed by the cluster heads (CHs) in a cluster-based wireless sensor network (WSN) in order to receive and aggregate the information from member sensor nodes. The choice of CHs has been an important factor in the amount of energy that sensor nodes have needed to use in order to make WSNs last longer. Particle Swarm Optimization (PSO) is one of the intelligent strategies that are proposed in "An Energy Efficient Unequal fault tolerant clustering approach," also known as "PSO-FL-C," with the purpose of lowering the amount of energy that is used. An optimal MCH-MDA selection technique has been given in the approach. This method makes use of the fitness values, and it evaluates these values based on the PSO to find two optimal nodes in each cluster that will operate as the Master Data Aggregator (MDA), and the MCH. The selected MCH is the only one who can collect the data from the cluster members, and the MDA is the one who is responsible for receiving the data from the MCH before sending it on to the BS. As a result, there is a decrease in the MCH overhead. During times of intense data transfer, overhead controls are implemented through the utilization of energy-efficient time division multiple access (EE-TDMA) scheduling. In order to demonstrate the superiority of the suggested approaches based on a variety of performance indicators, simulations will be run, and the results will be compared with those of the existing methods.