Wireless Link Simulations using Multi-level Markov Models

Ankur U. Kamthe, Miguel A. Carreira-Perpinan, Alberto E. Cerpa

Abstract

Modeling the behavior of 802.15.4 links is a non-trivial problem because of the widespread heterogeneity in the quality of any given link over short and long time scales. We propose a novel multilevel approach involving Hidden Markov Models (HMMs) and Mixtures of Multivariate Bernoullis (MMBs) for modeling the long and short time scale behavior of wireless links using experimental data traces collected from multiple 802.15.4 testbeds. We characterize the synthetic traces generated from the proposed model in terms of statistical characteristics as compared to an empirical trace with similar PRR characteristics.

Availability

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Reference

Ankur U. Kamthe, Miguel A. Carreira-Perpinan, Alberto E. Cerpa, "Wireless Link Simulations using Multi-level Markov Models," Proceedings of the Seventh ACM Conference on Embedded Network Sensor Systems (SenSys 2009), pp. 391--392, ACM, Berkeley, CA, USA, November, 2009.

Bibtex

@Conference{Kamthe09c,
  author =       "Ankur U. Kamthe and Miguel A. Carreira-Perpinan and
                 Alberto E. Cerpa",
  title =        "Wireless Link Simulations using Multi-level {M}arkov
                 {M}odels",
  booktitle =    "Proceedings of the Seventh ACM Conference on Embedded
                 Network Sensor Systems (SenSys 2009)",
  year =         "2009",
  pages =        "391--392",
  publisher =    "ACM",
  address =      "Berkeley, CA, USA",
  month =        nov,
  URL =          "http://www.andes.ucmerced.edu/papers/Kamthe09c.pdf",
}

Copyright

This paper is copyright © 2009 by its authors. Permission to make digital or hard copies of part or all of this work for personal use is granted without fee provided that copies are not made or distributed for profit or commercial purposes. New copies must bear this notice and the full citation on the first page. Abstracting with credit is permitted. To copy otherwise, to republish, to post on servers or to redistribute to lists, requires prior specific permission of the authors.