Stable Neighbor-Node Prediction with Multivariate Analysis in Mobile Ad Hoc Network Using RNN Model

Abstract

In mobile ad hoc networks (MANETs), mobile nodes are communicating with each other without use of any fixed infrastructure. Here, each node works as a receiver as well as transmitter point in the network. This network maintains the wireless connections with the neighbor nodes and establishes a connecting link between the source–destination (s-d) pair. The route in this type of network is highly unstable due to the mobility of the nodes. So, to construct a steady path between s-d pair, it is obvious to build a path through the stable neighbor nodes. In this article, we propose a stability index (SIN) which depends on the various parameters of the nodes such as past SIN values in different time intervals, node velocity, etc. In this paper, we establish a time series prediction model with multivariate analysis for predicting the stability index (SIN) of a node in reference to its neighbor nodes for the future time frame based on their past observation. For this purpose, we use the Elman recurrent neural network (ERNN)-based learning tool to determine the behavior of the mobile nodes of the network in the future time frame.