SVML-HA - Support vector machine learning based hop count analysis for distributing the routes in wireless sensor network

Abstract

Wireless Sensor Network WSN has played a crucial role in the modern wireless and communication system An optimal routing performance will significantly increase the wireless sensor network lifetime requiring higher power demand in routing Support vector machine SVM is the learning models are monitored in the relevant learning algorithms that analyze the data used for the vector engine classification and regression analysis It is often used in classification issues However the closer inquiry based energy learning performance indicates that all forms of deterioration are the root cause While dedicated study has seen over a decade more than a wireless sensor network energy solving problems related to efficient communication but still the energy efficient routing problem remains unresolved So only to design Support Vector Machine Learning based Hopcount analysis SVMLHA for distributing the routes in network Normally the number of routers that occur between both the Hop Count Source and the Target Network At the same time SVMLHA is a dynamic routing protocol used by the Metric Hop Count to guide the best route between both the source and destination in network The path with the low hop count is considered the best way to reach a network so the routing table will be placed The SVMLHA source and the destination are permitted on a path to prevent routing loops within the limit of numbers The maximum hop count allowed SVMLHA is access the 15th and 16th Hop Count network Information can easily be shared on the route network Our method of conducting research is analyzed by the use of network simulation