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High variability of access resources in heterogenous wireless networks and limited computing power and battery life of mobile computing devices such as smartphones call for novel approaches to satisfy the quality-of-service requirements of emerging wireless services and applications. Towards this end, the authors first investigate a Markov-based stochastic scheme for modeling and estimation of bandwidth and delay on heterogenous wireless networks. Borrowing clustering techniques from machine learning literature for intelligent state quantization, the authors demonstrate that the performance of the Markov model is enhanced significantly. They implement a measurement tool Zeus on smartphones and collect real-world data on 802.11g, 2.5G, and 3G wireless networks.
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