Energy Efficiency in Wireless Sensor Networks using Advanced LEACH Protocol

Abstract

The wireless sensor network is the best research subject matter with huge applications in various domains. By and large, a wireless sensor network involves hundreds to thousands of sensor nodes, which transmit and communicate with each other by the utilization of radio transmissions or signals. A portion of the difficulties that exist in the sketch of wireless sensor networks are confined computation power, storage capacity, battery, and transmission transfer speed or bandwidth. To determine these issues, clustering and routing algorithm have been introduced. Clustering and routing processes are viewed as an optimization issue in wireless sensor networks which can be settled by the utilization of swarm intelligence-based approaches. This article presents an original multitude of insight-based grouping and multihop routing protocol for wireless sensor report giving a new swarm optimization technique applied for picking the cluster heads and organizing the cluster capably. Then, at that point, the grey wolf optimization algorithm-based routing process takes place to choose the ideal ways in the network. The introduced better particle swarm optimization-grey wolf optimization approach consolidates the advantages of both the clustering and routing processes which prompts the greatest energy efficiency and network lifetime. The proposed model is reproduced under a broad arrangement of experiments, and the outcomes are approved under a few measures. The acquired trial result exhibited the predominant qualities of the improved particle swarm optimization–grey wolf optimization method under all the test cases. It enhances LEACH protocols in terms of energy efficiency, network Lifetime, and throughput. Maintaining the Energy Efficiency of a wireless sensor network has been a great concern nowadays. The main aim is to overcome the drawback of Improvement of the energy efficiency of a wireless sensor network. It may be improved with the performance of an optimization algorithm using swarm Intelligence. The novelty of the project is Network lifetime and Energy Consumption. The future of this project is to share the complete information which is present in the cluster head without any interruption