Hybrid Protocol for Cluster Head Selection for Energy Efficient Routing in WSN

The paper introduces Hybrid protocol for Wireless Sensor Networks (WSN) which is modification of LEACH protocol using Machine learning. The protocol considers traffic awareness, energy levels, and distance metrics to optimize the performance of the network. This protocol uses the K-medoid algorithm for initial clustering. The K-medoid algorithm is known for its ability to form clusters effectively based on data points distances. This approach uses various factors such as distance, traffic load, and energy levels to make intelligent decisions in choosing the most suitable candidate nodes as cluster heads. In re-cluster head selection, Support vector machine (SVM) is used. The performance is compared with the Low Energy Adaptive Clustering Hierarchy (LEACH) protocol, which is a widely used clustering protocol in WSNs. The results of the performance evaluation demonstrate that ML-EERP achieves significant improvements in energy efficiency compared to the traditional LEACH protocol. This highlights the effectiveness of utilizing machine learning techniques in the selection of cluster heads, leading to enhanced energy efficiency and improved network performance. By adopting a machine learning-based approach, the proposed ML-EERP protocol offers a promising solution for addressing energy efficiency concerns in WSN.