A novel supervised learning based neighbor discovepy in MANET Mobility Prediction in MANET

Mobile ad hoc networks (MANETs) is a multi-hop wireless network having the capability to self-conFigure and self-organize when affected by node mobility. Routing becomes a problem in MANET because nodes continuously change their position due to movement. As a result, a packet path must be dynamically adapted according to the node movement. Most routing protocols in MANET are characterized by low packet success ratio and low throughput. This work proposes an effective routing algorithm assisted by artificial neural networks for mobility prediction in order to improve the routing performances, thereby increasing the packet success ratio and throughput.