• The Journal of Korean Institute of Communications and Information Sciences
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• v.36 no.4A
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• pp.380-387
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• 2011

While mobile IPTV services are supported through the mobile WiMAX networks, there must need some guaranteed bandwidth for the IPTV services especially if IPTV and non-IPTV services are simultaneously supported by the mobile WiMAX networks. The quality of an IPTV service definitely depends on the allocated bandwidth for that channel. However, due to the high quality IPTV services and to support of huge non-IPTV traffic over mobile WiMAX networks, it is not possible to guarantee the sufficient amount of the limited mobile WiMAX bandwidth for the mobile IPTV services every time. A Service Level Agreement (SLA) between the mobile IPTV service provider and mobile WiMAX network operator to reserve sufficient bandwidth for the IPTV calls can increase the satisfaction level of the mobile IPTV users. In this paper, we propose a SLA negotiation procedure for mobile IPTV users over mobile WiMAX networks. The Bandwidth Broker controls the allocated bandwidth for IPTV and non-IPTV users. The proposed dynamically reserved bandwidth for the IPTV services increases the IPTV user's satisfaction level. The simulation results state that, our proposed scheme is able to provide better user satisfaction level for the IPTV users.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.6 no.3
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• pp.794-814
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• 2012

We propose a novel method for improving Wi-Fi positioning accuracy while reducing the energy consumption of mobile devices. Our method presents three contributions. First, we jointly and intelligently select the optimal subset of access points for positioning via maximum mutual information criterion. Second, we further propose local discriminant embedding algorithm for nonlinear discriminative feature extraction, a process that cannot be effectively handled by existing linear techniques. Third, to reduce complexity and make input signal space more compact, we incorporate clustering analysis to localize the positioning model. Experiments in realistic environments demonstrate that the proposed method can lower energy consumption while achieving higher accuracy compared with previous methods. The improvement can be attributed to the capability of our method to extract the most discriminative features for positioning as well as require smaller computation cost and shorter sensing time.