K-Medoids Clustering Based Next Location Prediction in Wireless Local Area Network

Abstract

User mobility prediction in wireless network is being investigated from various angles to improve performance of the network. Access to user’s movement information such as time, direction, speed etc provides an opportunity for wireless networks to effectively manage resources to satisfy user needs. A next location prediction technique is required for transferring the existing connections of user to the next Access Point (AP) beforehand to ensure better Quality of Service (QoS) of the network. There are several techniques for next location prediction of mobile users in Wireless Local Area Network (WLAN) which include Indoor Next Location Prediction with Wi-Fi model, Extended Mobility Markov Chain Model, Hidden Markov Model and Mixed Membership Stochastic Blockmodel. In the Indoor Next Location Prediction with Wi-Fi model the area of prediction is fixed and small which makes this approach inefficient when the number of locations traversed by the mobile user is large. The paper addresses the issue of predicting the next location of mobile users in a Wireless Local Area Network (WLAN) when the area of prediction is vast. In this paper an intelligent clustering technique i.e., the K-Medoids clustering algorithm has been implemented on the indoor next location prediction, which is based on a Markov-chain model, for predicting the next location of a user when the number of locations traversed by the user is vast. The accuracy of prediction of mobile user’s next location by the proposed K-Medoids clustering based next location prediction technique ranges from 67% to 97%.