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Top1. Introduction
Wireless Sensor Network consists of a large number of sensor nodes that perform the sensing, communication, and data processing. This network is mostly used for various applications such as patient monitoring systems, pollution control systems, environment monitoring, forest fire detection, and surveillance system, etc. The main task of the sensor node is to collect the information from the physical environment and forward the information to the base station through the cluster head. The sensor nodes have limited energy resources for the communication process. Replacing or recharging these batteries is a critical problem for the sensor network. Energy efficiency is an essential feature to design the sensor network in data communication.
Clustering is an important technique to provide a solution for energy-efficient communication in WSN. Clustering is utilized to form the group of clusters and to identify the optimal cluster head selection. The whole network is classified into a group of clusters. Each cluster is managed by the cluster head. The main task for the cluster head is to collect and process the information from the sensors and forward the information to the base station. The cluster head requires more energy for handling and coordinating the activities in the cluster members. The selection of optimal cluster head is an NP-hard problem. Energy-efficient communication can be carefully designed by identifying the optimal cluster head to increase the lifetime of the network.
1.1 Background
Bacteria Foraging Algorithm (BFA) is a significant technique for biologically inspired algorithms. The biologically inspired algorithms are helpful to design the dynamic and adaptive schemes in the wireless sensor network. The complex system behavior is adapted to the new location without any failure. The group behavior of the insects uses the model to give the solution for the problem without external guidance.
LEACH-C (Heinzelman et al., 2002) is the most popular hierarchical clustering algorithm for wireless sensor networks. Bacteria Forging Algorithm is applied in the LEACH-C algorithm to find optimal cluster head. This optimization technique obtains more steps in the tumbling process and reaches the global optimum solution very slowly. This optimization method consumes more energy for calculating the fitness function and also directly affects the network’s lifetime. The performance of BFA is improved by using particle swarm optimization and differential evolution to find the suitable cluster head in wireless sensor networks for improving the network lifetime and minimizing the energy consumption.
The performance of the Bacteria Foraging Algorithm is improved by using a hybrid approach of the Bacteria Foraging algorithm with Particle Swarm Optimization and Differential Evolution technique (BFPSODE) to find the optimal cluster head in wireless sensor networks. The local best and global best locations are generated by Particle Swarm Optimization and the position of bacteria is fine-tuned by Differential Evolution. These values are utilized by the tumbling process of every bacterium. The proposed methodology is utilized to update the behavior of bacteria for reaching a good position of local best and global best. The proposed method is implemented in Network Simulator (NS-2.27) and to measure the performance of the WSN. This hybrid optimization method improves the network lifetime and reduces energy consumption.