Improving QoS Parameters for Clustering in MANET using Grey Wolf Optimization and Global Algorithm Technique

Abstract

Manet have several nodes that are linked together wirelessly and the nodes are mobile in nature. Nodes position is flexible. The primary issue with the traditional clustering method is that it is prone to become trapped in the regional optimum path. While the nodes are being transmitted or received, energy is used. Through the use of the GWOGA clustering technique, CHs collect data from cluster participants and communicate other nodes with the cluster. While choosing the best CH to lengthen the network's lifespan is the Manet is most vital responsibility. Clustering based on the Grey Wolf Optimization and Global Algorithm (GWOGA), an attempt has been made to address this issue. GWOGA search function is employed for the best cluster centres in the supplied feature span. The cluster centres are encoded using the agent representation. When choosing the best CHs, the GWOGA dynamically balances the process of increasing and diversifying search. In addition, choosing the best CHs for the network is aided by factors like node degree, energy, distance, node centrality, Throughput, Delay, Path Loss Ratio, etc. The computational findings show that GWOGA offers improved values of excellent throughput, packet delivery ratio, end delay, energy a better lifespan segregated with the performance of normal clustering method. NS2 simulator is used for the simulation for finding QoS parameters.