Numerical Simulation and Performance Analysis of Improved LEACH Based Energy Efficient Wireless Sensor Network Routing Protocol

Abstract

Wireless Sensor Networks (WSNs) have gained significant attention in recent years due to their widespread applications in various domains, including environmental monitoring, healthcare, and industrial automation. One of the key challenges in WSNs is energy efficiency, as sensor nodes are often battery-powered and have limited energy resources. Efficient routing protocols play a vital role in prolonging the network's lifetime and ensuring reliable data transmission. This research focuses on the numerical simulation and performance analysis of a novel routing approach that leverages machine learning techniques to enhance energy efficiency in WSNs. The proposed routing algorithm adapts its decisions based on real-time environmental and network conditions, optimizing the energy consumption and overall network performance. We employ extensive numerical simulations to evaluate the performance of this machine learning-based routing protocol, comparing it with traditional routing protocols and highlighting the potential benefits in terms of energy conservation and data delivery reliability. Our simulations involve diverse scenarios, considering varying network topologies, traffic patterns, and environmental factors. We assess metrics such as energy consumption, network lifetime, packet delivery ratio, and end-to-end delay to provide a comprehensive analysis of the machine learning-based routing protocol's effectiveness. The results demonstrate the protocol's superiority in terms of energy efficiency, ensuring extended sensor node lifetimes and robust data delivery while adapting to dynamic network conditions.This study's findings underscore the potential of machine learning techniques to revolutionize the design and optimization of routing protocols in energy-constrained Wireless Sensor Networks. By improving energy efficiency and network performance, this research contributes to the development of sustainable and reliable WSNs, thereby facilitating the deployment of sensor networks in critical applications.