Comparative Analysis of ANN-Based Mobility Prediction Performance in an Ad-Hoc Network

Abstract

The facility of arbitrary node movement one side has advantages on application on the other side very difficult to manage the network because random node mobility directly effects on network connectivity and interrupt on the performance, obtained challenges like routing overhead, packet losses, increases energy consumption, wasted bandwidth for reconnection, decreases  throughput etc. Thus an accurate mobility prediction of a node before leaving one position to another or subsequence position can be improve network performance which is  effects by node mobility. Now day’s artificial neural networks (ANNs) is very common and trending for approximation and prediction application and also popular for node trajectory prediction. In this paper we explore the architectures of some static (like MLP and RBF) and dynamic (like FTDNN, DTDNN, NARX and LSTM) neural network and search best ANN model by obtaining optimal model parameters to predict node mobility and compared the performance using mobility model (Gauss Markov model) dataset as well as real-world dataset collected from Crawdad to highlight generalization capabilities. Mean square error (MSE), root mean square error (RMSE), mean absolute error (MAE), and average coordinates distance error (DE) between observed and estimated positions are used to evaluate their performance. The empirical results show that LSTM is the best artificial neural network (ANN) model for mobility prediction in both model based and real-world dataset(testing sets).