Assuring Robustness of Radio Spectrum Telemetry

Cognitive radios offer ways to better exploit unused radio spectrum if accurate spectrum availability data can be obtained.  Techniques based on signal propagation analysis and machine learning can limit the risks arising from bad spectrum data provided by malicious parties.

Recently the FCC has authorized the use of unused “white spaces” in the radio spectrum.  There are two primary strategies for determining whether spectrum is unused.  One of these is to produce a map of the geographic locations and frequencies that are claimed to be in use; the other is to use sensing technology in cognitive radios to collect reports dynamically.  The latter has some notable advantages, including the ability to do crowdsourcing of radio telemetry in which all willing sensors contribute data.  However, this type of data collection must be robust against malicious nodes that might vandalize spectrum by falsely reporting that it is not in use or exploit spectrum by falsely reporting that it is in use.

A University of Illinois PhD student, Omid Fatemieh, working with myself, Ranveer Chandra from Microsoft Research, and other PhD students, has demonstrated a range of techniques to limit the damage that can be caused by malicious misreporting of radio spectrum telemetry.  This work demonstrated the effectiveness of three primary strategies.  First, when sensing is done in small geographic cells it is possible to compare results from neighboring cells to corroborate reports and detect cells in which a majority of reports come from malicious sources.  Second, with suitable experimental data it is possible to use machine learning based on Support Vector Machines (SVMs) to create a classifier that can detect anomalies without the need for a specific radio propagation model.  Third, there are systematic ways to incorporate data from nodes that builds confidence in their trustworthiness such as remote attestation.  Fatemieh’s project included evaluations using TV transmitter data from the FCC, terrain data from NASA, and house density data from the US Census Bureau for areas of central  Illinois and southwestern Pennsylvania.  He conducted studies that demonstrated applications of the technology for advanced meter infrastructure in rural areas and for providing Internet access for public schools.

Dr. Fatemieh defended his dissertation in February 2011.  Here is a link to his home page.

  1. Reliable Telemetry in White Spaces using Remote Attestation, Omid Fatemieh, Michael LeMay, and Carl A. Gunter, ACSAC ’11.
  2. Assuring Robustness of Ratio Spectrum Telemetry Against Vandalism and Exploitation, Omid Fatemieh. Doctoral Thesis, University of Illinois at Urbana-Champaign, February 2011.
  3. Using Classification to Protect the Integrity of Spectrum Measurements in White Space Networks, Omid Fatemieh, Ali Farhadi, Ranveer Chandra and Carl A. Gunter, NDSS ’11.
  4. Low Cost and Secure Smart Meter Communications using the TV White Spaces, Omid Fatemieh, Ranveer Chandra and Carl A. Gunter, ISRCS ’10.
  5. Secure Collaborative Sensing for Crowdsourcing Spectrum Data in White Space Networks, Omid Fatemieh, Ranveer Chandra and Carl A. Gunter, DySPAN ’10.
The following images illustrate the nature of cells and readings and the architecture of the proposed wireless meter communication infrastructure respectively.