A Review Paper on the Application of Machine Learning for Ad-Hoc Network

Abstract

Mobile ad hoc networks collect wireless technology that enhances the ad hoc network in various situations, such as difficult releases, critical consultation or military duty, and even a lack of network infrastructure maintenance. Due to the fact that nodes can join or leave the network at your discretion, the network's topology may vary often. Nodes synchronize in mobile ad hoc networks to keep in touch with one another. Data is transferred from the source to the destination via central nodes. A node has dual functionality-host and router. This article outlines the most efficient method for moving nodes efficiently between sources and destinations while lowering computing costs and raising acquisition precision. Researchers use machine learning to solve issues with temporary networks and different mobile ad hoc network agreements in this study and the conversation. Many machine learning techniques that are used in wireless ad hoc networks are described, along with how they extract the most important criteria, restore them, and identify where they are. The most significant recent and continuing research in this area is also summarized in this paper.