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

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Advances in Signal Processing and Intelligent Recognition Systems (SIRS 2018)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 968))

Abstract

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.

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Notes

  1. 1.

    That is \(|\theta _{1}-\theta _{0}|=c/\sqrt{K}\) for a certain value \(c>0\) [27].

  2. 2.

    By doing a slight notation abuse, we adopt the notation \(\mathrm {I}(\theta ,\varvec{x}_{T},\varvec{\tau })\) (resp. \(\psi _{k,0}(\tau _{k})\) and \(\beta _{k,0}(\tau _{k})\)) in the place of \(\mathrm {I}(\theta ,\varvec{x}_{T})\) (resp. \(\psi _{k,0}\) and \(\beta _{k,0}\)) to highlight their parametric dependence on the local thresholds \(\tau _{k}\)’s.

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Author information

Authors and Affiliations

  1. University of Naples “Federico II”, Naples, Italy

    Giampaolo Bovenzi, Valerio Persico & Antonio Pescapè

  2. Network Measurement and Monitoring (NM2), Naples, Italy

    Domenico Ciuonzo, Valerio Persico & Antonio Pescapè

  3. Kongsberg Digital AS, Trondheim, Norway

    Pierluigi Salvo Rossi

Authors
  1. Giampaolo Bovenzi
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  2. Domenico Ciuonzo
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  3. Valerio Persico
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  4. Antonio Pescapè
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  5. Pierluigi Salvo Rossi
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Corresponding author

Correspondence to Domenico Ciuonzo .

Editor information

Editors and Affiliations

  1. Indian Institute of Information Technology and Management, Kerala, India

    Sabu M. Thampi

  2. Department of Computer and Electrical Engineering and Computer Science, Florida Atlantic University, Boca Raton, FL, USA

    Oge Marques

  3. Department of Electrical and Computer Engineering, Ryerson University, Toronto, ON, Canada

    Sri Krishnan

  4. Department of Computer Science and Information Engineering, Providence University, Taichung, Taiwan

    Kuan-Ching Li

  5. University of Naples Federico II, Naples, Italy

    Domenico Ciuonzo

  6. Electrical Engineering Department, Indian Institute of Technology Patna, Patna, India

    Maheshkumar H. Kolekar

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Bovenzi, G., Ciuonzo, D., Persico, V., Pescapè, A., Rossi, P.S. (2019). IoT-Enabled Distributed Detection of a Nuclear Radioactive Source via Generalized Score Tests. In: Thampi, S., Marques, O., Krishnan, S., Li, KC., Ciuonzo, D., Kolekar, M. (eds) Advances in Signal Processing and Intelligent Recognition Systems. SIRS 2018. Communications in Computer and Information Science, vol 968. Springer, Singapore. https://doi.org/10.1007/978-981-13-5758-9_7

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  • DOI: https://doi.org/10.1007/978-981-13-5758-9_7

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