IoT-Enabled Distributed Detection of a Nuclear Radioactive Source via Generalized Score Tests

  • Giampaolo Bovenzi
  • Domenico CiuonzoEmail author
  • Valerio Persico
  • Antonio Pescapè
  • Pierluigi Salvo Rossi
Conference paper
Part of the Communications in Computer and Information Science book series (CCIS, volume 968)


A decentralized detection method is proposed for revealing a radioactive nuclear source with unknown intensity and at unknown location, using a number of cheap radiation counters, to ensure public safety in smart cities. In the source present case, sensors nodes record an (unknown) emitted Poisson-distributed radiation count with a rate decreasing with the sensor-source distance (which is unknown), buried in a known Poisson background and Gaussian measurement noise. To model energy-constrained operations usually encountered in an Internet of Things (IoT) scenario, local one-bit quantizations are made at each sensor over a period of time. The sensor bits are collected via error-prone binary symmetric channels by the Fusion Center (FC), which has the task of achieving a better global inference. The considered model leads to a one-sided test with parameters of nuisance (i.e., the source position) observable solely in the case of \(\mathcal {H}_{1}\) hypothesis. Aiming at reducing the higher complexity requirements induced by the generalized likelihood ratio test, Davies’ framework is exploited to design a generalized form of the locally optimum detection test and an optimization of sensor thresholds (resorting to a heuristic principle) is proposed. Simulation results verify the proposed approach.


CBRN sensors Data fusion Distributed detection IoT Public safety Smart cities Wireless Sensor Networks 


  1. 1.
    Jin, J., Gubbi, J., Marusic, S., Palaniswami, M.: An information framework for creating a smart city through Internet of Things. IEEE Internet Things J. 1(2), 112–121 (2014)CrossRefGoogle Scholar
  2. 2.
    Varshney, P.K.: Distributed Detection and Data Fusion, 1st edn. Springer, New York (1996). Scholar
  3. 3.
    Coaffee, J., Moore, C., Fletcher, D., Bosher, L.: Resilient design for community safety and terror-resistant cities. In: Proceedings of the Institution of Civil Engineers-Municipal Engineer, vol. 161, pp. 103–110. Thomas Telford Ltd. (2008)Google Scholar
  4. 4.
    Brennan, S.M., Mielke, A.M., Torney, D.C., MacCabe, A.B.: Radiation detection with distributed sensor networks. IEEE Comput. 37(8), 57–59 (2004)CrossRefGoogle Scholar
  5. 5.
    Brennan, S.M., Mielke, A.M., Torney, D.C.: Radioactive source detection by sensor networks. IEEE Trans. Nucl. Sci. 52(3), 813–819 (2005)CrossRefGoogle Scholar
  6. 6.
    Stephens, D.L., Peurrung, A.J.: Detection of moving radioactive sources using sensor networks. IEEE Trans. Nucl. Sci. 51(5), 2273–2278 (2004)CrossRefGoogle Scholar
  7. 7.
    Pahlajani, C.D., Poulakakis, I., Tanner, H.G.: Networked decision making for Poisson processes with applications to nuclear detection. IEEE Trans. Autom. Control 59(1), 193–198 (2014)CrossRefGoogle Scholar
  8. 8.
    Pahlajani, C.D., Sun, J., Poulakakis, I., Tanner, H.G.: Error probability bounds for nuclear detection: improving accuracy through controlled mobility. Automatica 50(10), 2470–2481 (2014)MathSciNetCrossRefGoogle Scholar
  9. 9.
    Morelande, M., Ristic, B., Gunatilaka, A.: Detection and parameter estimation of multiple radioactive sources. In: 10th International Conference on Information Fusion (FUSION), pp. 1–7 (2007)Google Scholar
  10. 10.
    Morelande, M.R., Ristic, B.: Radiological source detection and localisation using Bayesian techniques. IEEE Trans. Signal Process. 57(11), 4220–4231 (2009)MathSciNetCrossRefGoogle Scholar
  11. 11.
    Ristic, B., Morelande, M., Gunatilaka, A.: Information driven search for point sources of gamma radiation. Signal Process. 90(4), 1225–1239 (2010)CrossRefGoogle Scholar
  12. 12.
    Hoballah, I.Y., Varshney, P.K.: Distributed Bayesian signal detection. IEEE Trans. Inf. Theory 35(5), 995–1000 (1989)MathSciNetCrossRefGoogle Scholar
  13. 13.
    Reibman, A.R., Nolte, L.W.: Optimal detection and performance of distributed sensor systems. IEEE Trans. Aerosp. Electron Syst. 1, 24–30 (1987)CrossRefGoogle Scholar
  14. 14.
    Viswanathan, R., Varshney, P.K.: Distributed detection with multiple sensors - part I: fundamentals. Proc. IEEE 85(1), 54–63 (1997)CrossRefGoogle Scholar
  15. 15.
    Ciuonzo, D., Salvo Rossi, P., Willett, P.: Generalized Rao test for decentralized detection of an uncooperative target. IEEE Signal Process. Lett. 24(5), 678–682 (2017)CrossRefGoogle Scholar
  16. 16.
    Fang, J., Liu, Y., Li, H., Li, S.: One-bit quantizer design for multisensor GLRT fusion. IEEE Signal Process. Lett. 20(3), 257–260 (2013)CrossRefGoogle Scholar
  17. 17.
    Ciuonzo, D., Salvo Rossi, P.: Decision fusion with unknown sensor detection probability. IEEE Signal Process. Lett. 21(2), 208–212 (2014)CrossRefGoogle Scholar
  18. 18.
    Aalo, V.A., Viswanathan, R.: Multilevel quantisation and fusion scheme for the decentralised detection of an unknown signal. IEE Proc. Radar Sonar Navig. 141(1), 37–44 (1994)CrossRefGoogle Scholar
  19. 19.
    Chen, B., Jiang, R., Kasetkasem, T., Varshney, P.K.: Channel aware decision fusion in wireless sensor networks. IEEE Trans. Signal Process. 52(12), 3454–3458 (2004)MathSciNetCrossRefGoogle Scholar
  20. 20.
    Ciuonzo, D., Romano, G., Salvo Rossi, P.: Channel-aware decision fusion in distributed MIMO wireless sensor networks, decode-and-fuse vs. decode-then-fuse. IEEE Trans. Wireless Commun. 11(8), 2976–2985 (2012)Google Scholar
  21. 21.
    Niu, R., Varshney, P.K.: Performance analysis of distributed detection in a random sensor field. IEEE Trans. Signal Process. 56(1), 339–349 (2008)MathSciNetCrossRefGoogle Scholar
  22. 22.
    Chin, J.R., et al.: Identification of low-level point radioactive sources using a sensor network. ACM Trans. Sens. Netw. (TOSN) 7(3), 21 (2010)Google Scholar
  23. 23.
    Sen, S., et al.: Performance analysis of Wald-statistic based network detection methods for radiation sources. In: 19th International Conference on Information Fusion (FUSION), pp. 820–827 (2016)Google Scholar
  24. 24.
    Sundaresan, A., Varshney, P.K., Rao, N.S.V.: Distributed detection of a nuclear radioactive source using fusion of correlated decisions. In: 10th IEEE International Conference on Information Fusion (FUSION), pp. 1–7 (2007)Google Scholar
  25. 25.
    Sundaresan, A., Varshney, P.K., Rao, N.S.V.: Copula-based fusion of correlated decisions. IEEE Trans. Aerosp. Electron. Syst. 47(1), 454–471 (2011)CrossRefGoogle Scholar
  26. 26.
    Sundaresan, A., Varshney, P.K., Rao, N.S.V.: Distributed detection of a nuclear radioactive source based on a hierarchical source model. In: IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 2901–2904 (2009)Google Scholar
  27. 27.
    Kay, S.M.: Fundamentals of Statistical Signal Processing, Volume 2: Detection Theory. Prentice Hall PTR, Upper Saddle River (1998)Google Scholar
  28. 28.
    Niu, R., Varshney, P.K.: Joint detection and localization in sensor networks based on local decisions. In: 40th Asilomar Conference on Signals, Systems and Computers, pp. 525–529 (2006)Google Scholar
  29. 29.
    Shoari, A., Seyedi, A.: Detection of a non-cooperative transmitter in Rayleigh fading with binary observations. In: IEEE Military Communications Conference (MILCOM), pp. 1–5 (2012)Google Scholar
  30. 30.
    Kailkhura, B., Ray, P., Rajan, D., Yen, A., Barnes, P., Goldhahn, R.: Byzantine-resilient locally optimum detection using collaborative autonomous networks. In: IEEE International Workshop on Computational Advances in Multi-Sensor Adaptive Processing (CAMSAP) (2018)Google Scholar
  31. 31.
    Ciuonzo, D., Salvo Rossi, P.: Distributed detection of a non-cooperative target via generalized locally-optimum approaches. Inf. Fusion 36, 261–274 (2017)CrossRefGoogle Scholar
  32. 32.
    Davies, R.D.: Hypothesis testing when a nuisance parameter is present only under the alternative. Biometrika 74(1), 33–43 (1987)MathSciNetzbMATHGoogle Scholar
  33. 33.
    Ciuonzo, D., Papa, G., Romano, G., Salvo Rossi, P., Willett, P.: One-bit decentralized detection with a Rao test for multisensor fusion. IEEE Signal Process. Lett. 20(9), 861–864 (2013)CrossRefGoogle Scholar
  34. 34.
    Ciuonzo, D., Salvo Rossi, P.: Quantizer design for generalized locally optimum detectors in wireless sensor networks. IEEE Wirel. Commun. Lett. 7(2), 162–165 (2018)CrossRefGoogle Scholar

Copyright information

© Springer Nature Singapore Pte Ltd. 2019

Authors and Affiliations

  • Giampaolo Bovenzi
    • 1
  • Domenico Ciuonzo
    • 2
    Email author
  • Valerio Persico
    • 1
    • 2
  • Antonio Pescapè
    • 1
    • 2
  • Pierluigi Salvo Rossi
    • 3
  1. 1.University of Naples “Federico II”NaplesItaly
  2. 2.Network Measurement and Monitoring (NM2)NaplesItaly
  3. 3.Kongsberg Digital ASTrondheimNorway

Personalised recommendations