Cluster Head and Optimal Path Slection Using K-GA and T-FA Algorithms for Wireless Sensor Networks

Abstract

Wireless Sensor Network (WSN) is a system with huge number of sensors connected to one another by placing them in a specific area. Different issues with WSN includes (but not limited to) the coverage, network lifetime and aggregation. The lifetime of a network can be improved by the clustering with the reduction of energy consumption. Clustering will group the related type of sensors into a single place with a head sensor node for message aggregation and transmission between other nodes and Base Station (BS). The cluster head (CH) consume more energy, when aggregating and transmitting the data. With the suitable identification of CH, there will be a reduction in the consumption of energy and improves the life of Wireless Sensor Network to be more. This paper modifies the meta-heuristic algorithms for improving the network lifetime by choosing appropriate cluster head and optimal path. K-Genetic Algorithm (K-GA) is proposed for efficient cluster head selection. Initially, the sensors are clustered using k-means clustering based on their location and Genetic Algorithm has been applied to detect the best cluster head. For secure optimal routing, Trust based Firefly (T-FA) path selection algorithm is used. Extensive simulations are conducted on various circumstances. The results obtained on the simulation indicates that the proposed K-GA helps in determining the optimized head of the cluster and T-FA discovers the optimal paths which enriches the life of the network by reducing end-to-end delay compared to other techniques.