Spectrum Protection from Micro-transmissions Using Distributed Spectrum Patrolling

  • Mallesham DasariEmail author
  • Muhammad Bershgal Atique
  • Arani Bhattacharya
  • Samir R. Das
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11419)


RF spectrum is a limited natural resource under a significant demand and thus must be effectively monitored and protected. Recently, there has been a significant interest in the use of inexpensive commodity-grade spectrum sensors for large-scale RF spectrum monitoring. The spectrum sensors are attached to compute devices for signal processing computation and also network and storage support. However, these compute devices have limited computation power that impacts the sensing performance adversely. Thus, the parameter choices for the best performance must be done carefully taking the hardware limitations into account. In this paper, we demonstrate this using a benchmarking study, where we consider the detection an unauthorized transmitter that transmits intermittently only for very small durations (micro-transmissions). We characterize the impact of device hardware and critical sensing parameters such as sampling rate, integration size and frequency resolution in detecting such transmissions. We find that in our setup we cannot detect more than 45% of such micro-transmissions on these inexpensive spectrum sensors even with the best possible parameter setting. We explore use of multiple sensors and sensor fusion as an effective means to counter this problem.


Distributed spectrum monitoring Transmission detection 



This work is partially supported by NSF grant CNS-1642965 and a grant from MSIT, Korea under the ICTCCP Program (IITP-2017-R0346-16-1007).


  1. 1.
  2. 2.
    Bazerque, J.A., Giannakis, G.B.: Distributed spectrum sensing for cognitive radio networks by exploiting sparsity. IEEE Trans. Sig. Process. 58(3), 1847–1862 (2010)MathSciNetCrossRefGoogle Scholar
  3. 3.
    Calvo-Palomino, R., Giustiniano, D., Lenders, V., Fakhreddine, A.: Crowdsourcing spectrum data decoding. In: INFOCOM 2017-IEEE Conference on Computer Communications, pp. 1–9. IEEE (2017)Google Scholar
  4. 4.
    Calvo-Palomino, R., Pfammatter, D., Giustiniano, D., Lenders, V.: A low-cost sensor platform for large-scale wideband spectrum monitoring. In: Proceedings of the 14th International Conference on Information Processing in Sensor Networks, pp. 396–397. ACM (2015)Google Scholar
  5. 5.
    Chakraborty, A., Bhattacharya, A., Kamal, S., Das, S.R., Gupta, H., Djuric, P.M.: Spectrum patrolling with crowdsourced spectrum sensors. In: IEEE INFOCOM (2018)Google Scholar
  6. 6.
    Chakraborty, A., Das, S.R.: Measurement-augmented spectrum databases for white space spectrum. In: CoNEXT, pp. 67–74. ACM (2014)Google Scholar
  7. 7.
    Chakraborty, A., Gupta, U., Das, S.R.: Benchmarking resource usage for spectrum sensing on commodity mobile devices. In: Proceedings of the 3rd Workshop on Hot Topics in Wireless, HotWireless 2016, pp. 7–11. ACM, New York (2016)Google Scholar
  8. 8.
    Chakraborty, A., Rahman, Md.S., Gupta, H., Das, S.R.: SpecSense: crowdsensing for efficient querying of spectrum occupancy. In: INFOCOM, pp. 1–9. IEEE (2017)Google Scholar
  9. 9.
    Chen, R., Park, J.-M., Bian, K.: Robust distributed spectrum sensing in cognitive radio networks. In: INFOCOM, pp. 1876–1884. IEEE (2008)Google Scholar
  10. 10.
    Cordeiro, C., Challapali, K., et al.: Spectrum agile radios: utilization and sensing architectures. In: DySPAN, pp. 160–169. IEEE (2005)Google Scholar
  11. 11.
    Dasari, M., Kelton, C., Nejati, J., Balasubramanian, A., Das, S.R.: Demystifying hardware bottlenecks in mobile web quality of experience. In: Proceedings of the SIGCOMM Posters and Demos, pp. 43–45. ACM (2017)Google Scholar
  12. 12.
    Dasari, M., Vargas, S., Bhattacharya, A., Balasubramanian, A., Das, S.R., Ferdman, M.: Impact of device performance on mobile internet QOE. In: Proceedings of the Internet Measurement Conference 2018, pp. 1–7. ACM (2018)Google Scholar
  13. 13.
    NASA RF Propagation Database.
  14. 14.
    MTP Group et al.: Microsoft Spectrum Observatory, Seattle, November 2013Google Scholar
  15. 15.
    Iyer, A., Chintalapudi, K., Navda, V., Ramjee, R., Padmanabhan, V.N., Murthy, C.R.: SpecNet: spectrum sensing sans frontieres. In: NSDI, pp. 351–364. USENIX Association (2011)Google Scholar
  16. 16.
  17. 17.
    Khaledi, M., et al.: Simultaneous power-based localization of transmitters for crowdsourced spectrum monitoring. In: Proceedings of the 23rd Annual International Conference on Mobile Computing and Networking, pp. 235–247. ACM (2017)Google Scholar
  18. 18.
    Kleber, N., et al.: RadioHound: a pervasive sensing platform for sub-6 GHZ dynamic spectrum monitoring. In: 2017 IEEE International Symposium on Dynamic Spectrum Access Networks (DySPAN), pp. 1–2. IEEE (2017)Google Scholar
  19. 19.
    Li, Z., et al.: Identifying value in crowdsourced wireless signal measurements. In: WWW, pp. 607–616. International World Wide Web Conferences Steering Committee (2017)Google Scholar
  20. 20.
    McPherson, A.P., Jack, R.H., Moro, G., et al.: Action-sound latency: are our tools fast enough? (2016)Google Scholar
  21. 21.
  22. 22.
    Nika, A., et al.: Empirical validation of commodity spectrum monitoring. In: SenSys, pp. 96–108. ACM (2016)Google Scholar
  23. 23.
    Nika, A., et al.: Towards commoditized real-time spectrum monitoring. In: Proceedings of the 1st ACM Workshop on Hot Topics in Wireless, pp. 25–30. ACM (2014)Google Scholar
  24. 24.
  25. 25.
  26. 26.
    Rajendran, S., et al.: ElectroSense: open and big spectrum data. IEEE Commun. Mag. 56(1), 210–217 (2018)CrossRefGoogle Scholar
  27. 27.
  28. 28.
    Saeed, A., Harras, K.A., Zegura, E., Ammar, M.: Local and low-cost white space detection. In: 2017 IEEE 37th International Conference on Distributed Computing Systems (ICDCS), pp. 503–516. IEEE (2017)Google Scholar
  29. 29.
    Schwaller, B.: Investigating, optimizing, and emulating candidate architectures for on-board space processing. Ph.D. thesis, University of Pittsburgh (2018)Google Scholar
  30. 30.
    Van den Bergh, B., et al.: ElectroSense: crowdsourcing spectrum monitoring. In: DySPAN, pp. 1–2. IEEE (2017)Google Scholar
  31. 31.
    Yucek, T., Arslan, H.: A survey of spectrum sensing algorithms for cognitive radio applications. IEEE Commun. Surv. Tutor. 11(1), 116–130 (2009)CrossRefGoogle Scholar
  32. 32.
    Zhang, T., Leng, N., Banerjee, S.: A vehicle based measurement framework for enhancing whitespace spectrum databases. In: MobiCom, pp. 17–28. ACM (2014)Google Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Mallesham Dasari
    • 1
    Email author
  • Muhammad Bershgal Atique
    • 1
  • Arani Bhattacharya
    • 1
  • Samir R. Das
    • 1
  1. 1.Stony Brook UniversityStony BrookUSA

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