Non-parametric passive traffic monitoring in cognitive radio networks

Qiben Yan, Ming Li, Feng Chen, Tingting Jiang, Wenjing Lou, Y. Thomas Hou, Chang Tien Lu

Research output: Chapter in Book/Report/Conference proceedingConference contribution

13 Scopus citations


Passive monitoring by distributed wireless sniffers has been used to strategically capture the network traffic, as the basis of automatic network diagnosis. However, the traditional monitoring techniques fall short in cognitive radio networks (CRNs) due to the much larger number of channels to be monitored, and the secondary users' channel availability uncertainty imposed by primary user activities. To better serve CRNs, we propose a systematic passive monitoring framework for traffic collection using a limited number of sniffers in Wi-Fi like CRNs. We jointly consider primary user activity and secondary user channel access pattern to optimize the traffic capturing strategy. In particular, we exploit a non-parametric density estimation method to learn and predict secondary users' access pattern in an online fashion, which rapidly adapts to the users' dynamic behaviors and supports accurate estimation of merged access patterns from multiple users. We also design near-optimal monitoring algorithms that maximize two levels of quality-of-monitoring goals respectively, based on the predicted channel access patterns. The simulations and experiments show that our proposed framework outperforms the existing schemes.

Original languageEnglish (US)
Title of host publication2013 Proceedings IEEE INFOCOM 2013
Number of pages9
StatePublished - 2013
Externally publishedYes
Event32nd IEEE Conference on Computer Communications, IEEE INFOCOM 2013 - Turin, Italy
Duration: Apr 14 2013Apr 19 2013

Publication series

NameProceedings - IEEE INFOCOM
ISSN (Print)0743-166X


Other32nd IEEE Conference on Computer Communications, IEEE INFOCOM 2013

ASJC Scopus subject areas

  • General Computer Science
  • Electrical and Electronic Engineering


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