A Novel Predictive and Non-Predictive Cooperative Model for Routing in Ad Hoc Networks

TL;DR

This paper introduces a new artificial neural network-based model to identify cooperative nodes in ad hoc networks, aiming to enhance route stability and network performance by predicting node behavior during route discovery.

Contribution

It presents a novel predictive and non-predictive cooperative model using neural networks to select stable routes based on multiple network parameters.

Findings

Model effectively predicts cooperative nodes in various network scenarios.

Simulation results show improved network stability and performance.

The approach adapts to different network types through adjustable weight factors.

Abstract

Adhoc networks are formed by intermediate nodes which agree to relay traffic.The link between nodes is broken when a node rejects to relay traffic. Various parameters like depreciation in the energy of a node, distance between nodes and mobility of the nodes play a vital role in determining the nodes rejection to relay traffic.The objective of this paper is to propose a novel model that identifies the cooperative nodes forming stable routes at the route discovery phase.The weight factor of the different parameters decides the varied type of networks where the proposed model can be applied. Hence, an Artificial Neural Network based nondeterministic generic predictive model is proposed to identify the varied types of networks based on the weight factor.This study has been substantiated by simulation using OMNET++ simulator. We are sure that this paper will give a better solution to identify cooperative nodes thereby improving the performance of the network.