Mobility Prediction in Wireless Ad Hoc Networks using Neural Networks

TL;DR

This paper presents a neural network-based approach for predicting mobility in wireless ad hoc networks to enhance routing stability and reduce connection disruptions.

Contribution

It introduces a novel neural network method utilizing recurrent networks and backpropagation through time for mobility prediction in ad hoc networks.

Findings

Improved path stability predictions in simulations.

Reduced routing overhead and connection interruptions.

Demonstrated effectiveness of neural networks in mobility prediction.

Abstract

Mobility prediction allows estimating the stability of paths in a mobile wireless Ad Hoc networks. Identifying stable paths helps to improve routing by reducing the overhead and the number of connection interruptions. In this paper, we introduce a neural network based method for mobility prediction in Ad Hoc networks. This method consists of a multi-layer and recurrent neural network using back propagation through time algorithm for training.