Dynamic resource modeling for heterogeneous wireless networks

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, we 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, we demonstrate that the performance of the Markov model is enhanced significantly. We implement a measurement tool Zeus on smartphones and collect real-world data on 802.11g, 2.5G, and 36 wireless networks. The accuracy of the developed model is evaluated through simulation studies based on the collected data. Furthermore, a distributed rate-control scheme leveraging the predictions of our model is developed and observed to be much more efficient than a baseline additive-increase multiplicativedecrease scheme.