Delay Prediction for Intelligent Routing in Wireless Networks Using Neural Networks

Abstract

Research on wireless routing protocols have been striving to achieve two important but contradicting goals: adaptability to the dynamic network conditions and efficient routing information diffusion. A possible approach to solve this issue is through intelligent use of the nodes' past experiences of the network traffic conditions and making predictions on the future network traffic conditions based on the experiences. Delay is a typical routing metric. In this paper, we present a scheme for predicting mean per-packet one-hop delays using neural network approaches. The predicted one-hop delays are then used by the nodes to participate in routing information diffusion. By experiments, we prove the feasibility of predicting mean delays as a time series using either tapped-delay-line Multi-Layer Perceptron (MLP) network or tapped-delay-line Radial Basis Function (RBF) network. Two types of inputs for prediction are used: a) the mean delay time series itself only, b) the mean delay time series together with the corresponding traffic loads. The advantages and limitations of these neural network approaches are discussed.