Prediction of State of Wireless Network Using Markov and Hidden Markov Model

Abstract

Optimal resource allocation and higher quality of service is a much needed requirement in case of wireless networks. In order to improve the above factors, intelligent prediction of network behavior plays a very important role. Markov Model (MM) and Hidden Markov Model (HMM) are proven prediction techniques used in many fields. In this paper, we have used Markov and Hidden Markov prediction tools to predict the number of wireless devices that are connected to a specific Access Point (AP) at a specific instant of time. Prediction has been performed in two stages. In the first stage, we have found state sequence of wireless access points (AP) in a wireless network by observing the traffic load sequence in time. It is found that a particular choice of data may lead to 91% accuracy in predicting the real scenario. In the second stage, we have used Markov Model to find out the future state sequence of the previously found sequence from first stage. The prediction of next state of an AP performed by Markov Tool shows 88.71% accuracy. It is found that Markov Model can predict with an accuracy of 95.55% if initial transition matrix is calculated directly. We have also shown that O(1) Markov Model gives slightly better accuracy in prediction compared to O(2) MM for predicting far future.