Hidden Markov modeling for network communication channels
Proceedings of the 2001 ACM SIGMETRICS international conference on Measurement and modeling of computer systems
Mean Shift: A Robust Approach Toward Feature Space Analysis
IEEE Transactions on Pattern Analysis and Machine Intelligence
End-to-end available bandwidth: measurement methodology, dynamics, and relation with TCP throughput
Proceedings of the 2002 conference on Applications, technologies, architectures, and protocols for computer communications
A Markov-based channel model algorithm for wireless networks
Wireless Networks
A measurement study of available bandwidth estimation tools
Proceedings of the 3rd ACM SIGCOMM conference on Internet measurement
A machine learning approach to TCP throughput prediction
Proceedings of the 2007 ACM SIGMETRICS international conference on Measurement and modeling of computer systems
Robust Rate Control for Heterogeneous Network Access in Multihomed Environments
IEEE Transactions on Mobile Computing
Game theoretic rate control for mobile devices
GameNets'09 Proceedings of the First ICST international conference on Game Theory for Networks
SDL+QualNet: a novel simulation environment for wireless heterogeneous networks
Proceedings of the 3rd International ICST Conference on Simulation Tools and Techniques
Content-aware rate allocation for efficient video streaming via dynamic network utility maximization
Journal of Network and Computer Applications
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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, 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 3G 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 multiplicative-decrease scheme.