Advertisement

Measuring Spectrum Similarity in Distributed Radio Monitoring Systems

  • Roberto Calvo-PalominoEmail author
  • Domenico GiustinianoEmail author
  • Vincent LendersEmail author
Conference paper
Part of the Communications in Computer and Information Science book series (CCIS, volume 766)

Abstract

The idea of distributed spectrum monitoring with RF front-ends of a few dollars is gaining attention to capture the real-time usage of the wireless spectrum at large geographical scale. Yet the limited hardware of these nodes hinders some of the applications that could be envisioned. In this work, we exploit the fact that, because of its affordable cost, massive deployments of spectrum sensors could be foreseen in the early future, where the radio signal of one wireless transmitter is received by multiple spectrum sensors in range and connected over the public Internet. We envision that nodes in this scenario may collaboratively take decisions about which portion of the spectrum to monitor or not. A key problem for collaborative decision is to identify the conditions where the nodes receive the same spectrum data. We take an initial step in this direction, presenting a collaborative system architecture, and investigating the challenges to correlate pre-processed data in the backend, with key insights in the trade-offs in the system design in terms of network bandwidth and type of over-the-air radio signals. Our results suggest that it is feasible to determinate in the backend if two sensors are reading the same analog/digital signal in the same frequency, only sampling during 200 ms and sending just 1 KB of data per sensor to the backend.

Notes

Acknowledgments

This work has been funded in part by the TIGRE5-CM program (S2013/ICE-2919). We thank the anonymous reviewers for their valuable comments and suggestions.

References

  1. 1.
    Microsoft spectrum observatory. http://observatory.microsoftspectrum.com/
  2. 2.
    Naganawa, J., Kim, H., Saruwatari, S., Onaga, H., Morikawa, H.: Distributed spectrum sensing utilizing heterogeneous wireless devices and measurement equipment. In: 2011 IEEE Symposium on New Frontiers in Dynamic Spectrum Access Networks (DySPAN), pp. 173–184, May 2011Google Scholar
  3. 3.
    Iyer, A., Chintalapudi, K.K., Navda, V., Ramjee, R., Padmanabhan, V., Murthy, C.: Specnet: spectrum sensing sans frontiéres. In: 8th USENIX Symposium on Networked Systems Design and Implementation (NSDI). USENIX, March 2011Google Scholar
  4. 4.
    Nika, A., Zhang, Z., Zhou, X., Zhao, B.Y., Zheng, H.: Towards commoditized real-time spectrum monitoring. In: Proceedings of the 1st ACM Workshop on Hot Topics in Wireless, ser. HotWireless 2014, pp. 25–30. ACM, New York (2014)Google Scholar
  5. 5.
    Arcia-Moret, A., Pietrosemoli, E., Zennaro, M.: WhispPi: white space monitoring with Raspberry Pi. In: Global Information Infrastructure Symposium (GIIS 2013) (2013)Google Scholar
  6. 6.
    Grnroos, S., Nybom, K., Bjrkqvist, J., Hallio, J., Auranen, J., Ekman, R.: Distributed spectrum sensing using low cost hardware. J. Signal Process. Syst. Signal Image Video Technol. 113 (2015)Google Scholar
  7. 7.
    Pfammatter, D., Giustiniano, D., Lenders, V.: A software-defined sensor architecture for large-scale wideband spectrum monitoring. In: Proceedings of the 14th International Symposium on Information Processing in Sensor Networks, ser. IPSN 2015, Seattle, WA, USA, pp. 71–82 (2015)Google Scholar
  8. 8.
    Souryal, M., Ranganathan, M., Mink, J., Ouni, N.E.: Real-time centralized spectrum monitoring: feasibility, architecture, and latency. In: 2015 IEEE Symposium on Dynamic Spectrum Access Networks (DySPAN), September 2015Google Scholar
  9. 9.
    Calvo-Palomino, R., Giustiniano, D., Lenders, V.: Electrosense: crowdsourcing spectrum monitoring. In: 2017 IEEE International Conference on Computer Communications (Infocom), May 2017Google Scholar
  10. 10.
    Akyildiz, I.F., Lee, W.-Y., Vuran, M.C., Mohanty, S.: Next generation/dynamic spectrum access/cognitive radio wireless networks: a survey. Comput. Netw. 50(13), 2127–2159 (2006)CrossRefzbMATHGoogle Scholar
  11. 11.
    Axell, E., Leus, G., Larsson, E., Poor, H.: Spectrum sensing for cognitive radio : state-of-the-art and recent advances. IEEE Signal Process. Magaz. 29(3), 101–116 (2012)CrossRefGoogle Scholar
  12. 12.
    Quan, Z.Q.Z., Cui, S.C.S., Sayed, A.H., Poor, H.: Wideband spectrum sensing in cognitive radio networks. In: 2008 IEEE International Conference on Communications (2008)Google Scholar
  13. 13.
  14. 14.
    Zheleva, M., Chandra, R., Chowdhery, A., Kapoor, A., Garnett, P.: Txminer: identifying transmitters in real-world spectrum measurements. In: 2015 IEEE Symposium on Dynamic Spectrum Access Networks (DySPAN), September 2015Google Scholar
  15. 15.
    Mishra, S., Sahai, A., Brodersen, R.: Cooperative sensing among cognitive radios. In: 2006 IEEE International Conference on Communications, pp. 1658–1663 (2006)Google Scholar
  16. 16.
    Ghasemi, A., Sousa, E.S.: Collaborative spectrum sensing for opportunistic access in fading environments. In: 2005 1st IEEE International Symposium on New Frontiers in Dynamic Spectrum Access Networks (DySPAN 2005), pp. 131–136 (2005)Google Scholar
  17. 17.
    Cacciapuoti, A.S., Akyildiz, I.F., Paura, L.: Correlation-aware user selection for cooperative spectrum sensing in cognitive radio ad hoc networks. IEEE J. Sel. Areas Commun. 30(2), 297–306 (2012)CrossRefGoogle Scholar
  18. 18.
    Sampietro, M., Accomoando, G., Fasoli, L.G., Ferrari, G., Gatti, E.C.: High sensitivity noise measurement with a correlation spectrum analyzer. IEEE Trans. Instrum. Measurement 49(4), 1–3 (2000)CrossRefGoogle Scholar
  19. 19.
    Shi, L., Bahl, P., Katabi, D.: Beyond sensing: multi-GHZ realtime spectrum analytics. In: 12th USENIX Symposium on Networked Systems Design and Implementation (NSDI 2015). USENIX Association, Oakland (2015)Google Scholar

Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  1. 1.IMDEA Networks InstituteMadridSpain
  2. 2.Universidad Carlos III of MadridMadridSpain
  3. 3.ArmasuisseThunSwitzerland

Personalised recommendations