An analysis of the use of machine learning in ad hoc networks

Abstract

Mobile Ad hoc Networks collect wireless technology that enhances the ad hoc network in a
range of situations, such as difficult releases, critical consultation or military duty, and even a
lack of network infrastructure maintenance. The network's topology may vary on a regular
basis since nodes may join to or detach from the network at your discretion. To communicate
with one another, nodes in mobile ad hoc networks synchronize [1]. Central nodes transport
data from the source to the destination. A node may operate as both a host and a router. This
article describes the most effective approach for efficiently transferring nodes between
sources and destinations while reducing processing costs and increasing acquisition accuracy.
In this study and discussion, researchers employ machine learning to overcome problems
with temporary networks and various Mobile Ad hoc Network agreements. Many machine
learning (ML) approaches used in wireless ad hoc networks are presented, including how the
most significant criteria are extracted, restored, and identified [10]. This publication also
summarizes the most important recent and ongoing research in this field.