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Congestion Management of GSTEB Routing Protocol Using K-Means Algorithm in Wireless Sensor Network

A sensor node comprises of enormous nodes which can a variety of tasks such as recognize, indulgence, communication, and influence resource. Data transmission is the main aspect from starting to target. Once it is transferred between the node and within-cluster, the congestion will arise either node or in a resource allotment. So that it will affect the unremitting delay of reaching data, flow, the defeat of information to the destination, and the unfavorable use of a significant quantity of energy in the node. In this work, the impact of machine learning algorithms is studied using the GSTEB hierarchical protocol. By altering the type of input parameters, K-Means clustering on node energy usage can be performed. If congestion occurs, our proposal outlines a strategy for dealing with it and improving the CHPSCCC (Clustered Highly Prioritized Application-Specific Congestion Control Clustering Protocol) approach for congestion management. Simulation results show that in the proposed system there is significant growth of rises in the lifetime, energy, throughput, and packet loss.