IoT-enabled distributed detection of a nuclear radioactive source via generalized score tests

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 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.