Machine Learning Based CH Selection for Energy Efficient Routing in WSN

In wireless sensor networks (WSNs), energy efficiency is a crucial consideration due to the limited battery power of sensor nodes. This paper proposes a machine learning-based energy-efficient routing protocol (ML-EERP) that takes into account traffic awareness. The protocol incorporates initial clustering using the K-medoid algorithm and utilizes machine learning techniques for cluster head selection based on factors such as distance, traffic, and energy levels. The protocol's experimental analysis focuses on evaluating the effectiveness of selecting the most suitable candidate nodes as cluster heads and assessing their impact on energy efficiency. Comparative analysis is performed by comparing the results of the proposed machine learning-based protocol with the traditional LEACH protocol. The performance evaluation of the proposed protocol includes metrics such as network lifetime, throughput, packet delivery ratio, and energy consumption analysis. The experimental results demonstrate significant improvements in energy efficiency compared to the LEACH protocol. This highlights the effectiveness of using machine learning techniques in the selection of cluster heads, leading to enhanced energy efficiency and improved network performance. By employing a machine learning-based approach, the proposed protocol offers a promising solution for achieving energy efficiency in WSNs. It leverages the power of machine learning algorithms to adaptively select cluster heads based on various parameters, resulting in improved energy utilization and extended network lifespan.