Improving QoS of DYMO Routing Protocol in MANETs using Random Forest Classifier Technique

Abstract

A network known as mobile ad hoc network (MANET) is made up of several wireless mobile nodes that connect with one another without the need of constitutional   administration or network infrastructure. The mobile nodes have the ability to act as both the host and a router, sending packets from the source node to the destinations. Routing in MANETs is difficult since mobile nodes are constantly moving, the topology is always changing, and occasionally routes fail, resulting in a decline in network performance. Random Forest Classifier machine learning is an effective procedure for modifying productive routing protocols for MANETs. Aforementioned paper illustrates the characteristics and working of DYMO routing protocol. Further we implemented Random Forest Classifier technique on DYMO protocol using Google Collaboratory simulator and correlated its simulator results with elementary DYMO protocol.