Optimized access point selection with mobility prediction using hidden Markov Model for wireless network

Access point selection is an issue frequently faced by mobile user due to constant movement. By connecting to the best Wireless Local Area Network (WLAN) Access Point (AP), mobile users can enjoy the advantages of power consumption reduction while sustaining good communication quality. In this paper, a new approach to intelligently selecting access point in wireless local area network using Hidden Markov Model (HMM) is proposed. Hidden Markov Model is used as prediction tool to forecast the WLAN AP that can provide optimal Quality of Service (QoS) by observing the location histories of the mobile device. Besides, a location awareness AP selection algorithm is proposed to improve the number of connection to AP with a better signal quality. The effectiveness and performance of the proposed approach is evaluated through simulations and results showed that by using the proposed approach, the number of connection to high signal level AP increased and number of connection to low signal level AP decreased in comparison with conventional approach.