Futuristic Analysis of Machine Learning Based Routing Protocols in Wireless Ad Hoc Networks

With achievements of Machine learning over the past years many computer networks and artificial intelligence actively using Machine learning architecture and its technology to improvise the performance of their approach for effective output. Machine learning plays great role in the field of wireless ad-hoc network as by providing the suitable environment to the routing protocols to make them react accordingly so that will have maximum throughput and parameters such as packet delivery ratio, hop-to-hop count, optimization of quality of service (QoS) will increased. In this paper, various types of Machine learning applied on different wireless Ad-hoc network are studied and how the performance parameters vary accordingly. A systematic approach is used to study all simulators which are utilized and evaluated by different protocols used in MANET, VANET. There is a need of a many other parameters to be studied and simulation used in Ad-hoc network which enables the users to ensure the optimization so that we can minimize the chances of failure of data and maximize the throughput after selecting best model of machine learning. This paper presents the futuristic anatomy of different machine learning models used in various protocols.