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Abstract

Network selection is a common issue faced by mobile users. There are several approaches that can be used to optimize the quality of service (QoS) in wireless local area network (WLAN) and cellular network namely by connecting to optimal access point (AP) or using parallel path at transport layer such as transmission control protocol (TCP). In this paper, progressive mobility prediction (PMP) is used to predict the optimal WiFi AP that mobile devices should be connected to for better connectivity. In PMP, dual hidden Markov model (HMM) is used as a prediction tool to provide optimal QoS. The performance and effectiveness of the proposed PMP approach is evaluated in a real-world test bed and compared with MultiPath TCP (MPTCP), the protocol that is used to aggregate multiple network paths for better network performance. The results show that optimal access point prediction with PMP helps in the WiFi AP selection process compared with MPTCP and conventional approach. By selecting optimal WiFi AP, mobile users experience lower handover count as well as network throughput as compared to MPTCP and the conventional approach.

Keywords

Hidden Markov model user mobility access point prediction seamless connectivity wireless local area network

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Performance comparison between progressive mobility prediction using hidden Markov model and multipath transmission control protocol

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