Path length prediction in MANET under AODV routing: Comparative analysis of ARIMA and MLP model

Abstract

Mobile Ad-hoc network (MANET) is infra-structure less collection of mobile nodes which can communicate with each other through single hop or multi-hop technique. The hop count also known as path length plays a crucial role in packet delivery, routing load, delay, etc. The path length between source destination pair nodes depends upon factors such as the mobility patterns of nodes, routing algorithm, transmission range, etc. In this article, we have tried to predict the path length between a source destination pair in MANET using Autoregressive Integrated Moving Average (ARIMA) and multilayer perceptron (MLP) models. The path length data are collected from MANETs using three different mobility models namely (i) Manhattan Grid Mobility Model (MHG), (ii) Random Way Point mobility model (RWP) and (iii) Reference Point Group Mobility Model (RPGM). This paper evaluates the predictive accuracy in forecasting the path length between source and destination nodes for Ad hoc On-Demand Distance Vector AODV routing in MANET using ARIMA model and MLP. It is found that neural networks can be effectively used in forecasting path length between mobile nodes better than statistical model and the MLP based neural network models are found to be better forecaster than ARIMA model.