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The Scientific World Journal
Volume 2014, Article ID 610652, 17 pages
Research Article

An Adaptive Handover Prediction Scheme for Seamless Mobility Based Wireless Networks

1Faculty of Computer Systems and Software Engineering, Universiti Malaysia Pahang, Lebuhraya Tun Razak, Gambang, 26300 Kuantan, Pahang, Malaysia
2UTM MIMOS CoE in Telecommunication Technology, Faculty of Electrical Engineering, Universiti Teknologi Malaysia (UTM), 81310 Johor Bahru, Johor Darul Takzim, Malaysia
3Faculty of Engineering, Koya University, Danielle Mitterrand Boulevard, Koya, Kurdistan Region, Iraq
4Instituto de Investigación para la Gestión Integrada de Zonas Costeras, Universidad Politécnica de Valencia, 46022 Valencia, Spain

Received 15 June 2014; Accepted 14 September 2014; Published 4 December 2014

Academic Editor: Weifeng Sun

Copyright © 2014 Ali Safa Sadiq et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


We propose an adaptive handover prediction (AHP) scheme for seamless mobility based wireless networks. That is, the AHP scheme incorporates fuzzy logic with AP prediction process in order to lend cognitive capability to handover decision making. Selection metrics, including received signal strength, mobile node relative direction towards the access points in the vicinity, and access point load, are collected and considered inputs of the fuzzy decision making system in order to select the best preferable AP around WLANs. The obtained handover decision which is based on the calculated quality cost using fuzzy inference system is also based on adaptable coefficients instead of fixed coefficients. In other words, the mean and the standard deviation of the normalized network prediction metrics of fuzzy inference system, which are collected from available WLANs are obtained adaptively. Accordingly, they are applied as statistical information to adjust or adapt the coefficients of membership functions. In addition, we propose an adjustable weight vector concept for input metrics in order to cope with the continuous, unpredictable variation in their membership degrees. Furthermore, handover decisions are performed in each MN independently after knowing RSS, direction toward APs, and AP load. Finally, performance evaluation of the proposed scheme shows its superiority compared with representatives of the prediction approaches.