Efficient Routing Algorithm using MLP and RBX in a Four Model Neural Networks

Abstract

Two important paradigms which are contradicting by nature namely: the efficient routing information diffusion and adaptability to dynamic network conditions using wireless routing protocols have been researched in recent years. One way of solving this issue is by using the past experiences of a node in network traffic condition through intelligent algorithm to predict the network traffic condition in the future. In this methodology we propose an algorithm which is used to to predict one hop delay per packet during routing process using neural networking. The one hop delay that is predicted is then further used by the participating nodes for information diffusion during routing. Experimental analysis indicate that using tapped delay line radial basis function and tapped delay line multilayer perceptron, it is possible to predict mean delays as a time series. The inputs used for prediction are mean delay time series with traffic loads and mean delay time series itself. The pros and cons of the proposed work are also present in this paper.