Specmonitor: Toward efficient passive traffic monitoring for cognitive radio networks

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

Research output: Contribution to journalArticlepeer-review

7 Scopus citations

Abstract

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, i.e., SpecMonitor, 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 nonparametric 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 based on the predicted channel access patterns. The simulations and experiments show that SpecMonitor outperforms the existing schemes significantly.

Original languageEnglish (US)
Article number2339218
Pages (from-to)5893-5905
Number of pages13
JournalIEEE Transactions on Wireless Communications
Volume13
Issue number10
DOIs
StatePublished - Oct 1 2014
Externally publishedYes

Keywords

  • Cognitive radio network
  • Non-parametric density estimation
  • Optimization algorithm
  • Passive monitoring

ASJC Scopus subject areas

  • Computer Science Applications
  • Electrical and Electronic Engineering
  • Applied Mathematics

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