Various Detection Techniques and Platforms for Monitoring Interference Condition in a Wireless Testbed

  • Wei Liu
  • Stratos Keranidis
  • Michael Mehari
  • Jono Vanhie-Van Gerwen
  • Stefan Bouckaert
  • Opher Yaron
  • Ingrid Moerman
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7586)


Recently the constant growth of the wireless communication technology has caused a huge demand for experimental facilities. Hence many research institutes setup public accessible experimental facilities, known as testbeds. Compared to the facilities developed by individual researchers, a testbed typically offers more resources, more flexibilities. However, due to the fact that equipments are located remotely and experiments involve more complex scenarios, the required complexity for analysis is also higher. A deep insight on the underlying wireless environment of the testbed becomes necessary for comprehensive analysis.

In this chapter, we present a framework and associated techniques for monitoring the wireless environment in a large scale wireless testbed. The framework utilizes most common resources in the testbed, such as WI-FI nodes, as well as some high-end software-defined radio platforms. Information from both physical layer and network layer are taken into account. We observe that feature detection is more sensitive than general energy detection for dedicated technologies, and distributed spectrum sensing can further improve the detection sensitivity. Such observations are applied to achieve better interference detection. The performance is mainly analyzed experimentally.


Interference detection testbed spectrum sensing 


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    IEEE802.11 standards revised due to fast WI-FI growth,
  2. 2.
  3. 3.
    Burchfield, R., et al.: RF in the Jungle: Effect of Environment Assumptions on Wireless Experiment Repeatability. In: IEEE International Conference on Communications, ICC 2009, pp. 1–6 (2009), doi:10.1109/ICC.2009.5199421Google Scholar
  4. 4.
    Kamerman, A., Monteban, L.: WaveLAN-II: A High-performance wireless LAN for the unlicensed band. Bell Lab Technical Journal, 118–133 (Summer 1997)Google Scholar
  5. 5.
    Lacage, M., et al.: IEEE 802.11 rate adaptation: a practical approach. In: MSWiM 2004 Proceedings of the 7th ACM International Symposium on Modeling, Analysis and Simulation of Wireless and Mobile Systems, pp. 126–134 (2004)Google Scholar
  6. 6.
    Baccour, N., Koubâa, A., Youssef, H., Ben Jamâa, M., do Rosário, D., Alves, M., Becker, L.B.: F-LQE: A fuzzy link quality estimator for wireless sensor networks. In: Silva, J.S., Krishnamachari, B., Boavida, F. (eds.) EWSN 2010. LNCS, vol. 5970, pp. 240–255. Springer, Heidelberg (2010)CrossRefGoogle Scholar
  7. 7.
    Tevfik, Y., Huseyin, A.: A survey of spectrum sensing algorithms for cognitive radio applications. IEEE Comm. Servey and Tutorial 11(1), 116–130 (2009)CrossRefGoogle Scholar
  8. 8.
    Passas, V., Keranidis, S., Korakis, T., Koutsopoulos, I., Tassiulas, L.: An Experimental Framework for Channel Sensing through USRP/GNU Radios. In: Korakis, T., Zink, M., Ott, M. (eds.) TridentCom 2012. LNICST, vol. 44, pp. 383–386. Springer, Heidelberg (2012)CrossRefGoogle Scholar
  9. 9.
  10. 10.
    Syrivelis, D., Anadiotis, A.C., Apostolaras, A., Korakis, T., Tassiulas, L.: TLQAP: A Topology and Link Quality Assessment Protocol For Efficient Node Allocation on Wireless Testbeds. In: The Proceedings of WiNTECH 2009, Beijing, China (September 2009)Google Scholar
  11. 11.
  12. 12.
    Baccour, N., et al.: A testbed for the evaluation of link quality estimators in wireless sensor networks. In: IEEE/ACS International Conference on Computer Systems and Applications, AICCSA (2010)Google Scholar
  13. 13.
    Ettus Research,
  14. 14.
    Rakotoarivelo, T., Ott, M., Jourjon, G., Seskar, I.: OMF: A Control and Management Framework for Networking Testbeds. SIGOPS Oper. Syst. Rev. 43, 54–59 (2010)CrossRefGoogle Scholar
  15. 15.
  16. 16.
  17. 17.
    Sutton, P., et al.: Iris: an architecture for cognitive radio networking testbeds. IEEE Comm. Mag. 48(9), 114–122 (2010)CrossRefGoogle Scholar
  18. 18.
  19. 19.
    Liu, W., et al.: Real-time wide-band spectrum sensing for cognitive radio. In: 2011 18th IEEE Symposium on Communications and Vehicular Technology in the Benelux, SCVT (2011)Google Scholar
  20. 20.
    Lee, J., et al.: An experimental study on the capture effect in 802.11a networks. In: Proceedings of the Second ACM International Workshop on Wireless Network Testbeds, Experimental Evaluation and Characterization, pp. 19–26Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Wei Liu
    • 1
  • Stratos Keranidis
    • 2
    • 3
  • Michael Mehari
    • 1
  • Jono Vanhie-Van Gerwen
    • 1
  • Stefan Bouckaert
    • 1
  • Opher Yaron
    • 1
  • Ingrid Moerman
    • 1
  1. 1.Department of Information Technology, Internet Based Communication Networks and Services (IBCN)Ghent University - iMindsGentBelgium
  2. 2.Department of Computer and Communication EngineeringUniversity of ThessalyGreece
  3. 3.Centre for Research and Technology Hellas, CERTHGreece

Personalised recommendations