Detecting Nuclear Materials in Urban Environments Using Mobile Sensor Networks

Abstract

Radiation detectors installed at major ports of entry are a key component of the overall strategy to protect countries from nuclear terrorism. While the goal of deploying these systems is to intercept special nuclear material as it enters the country, no detector system is foolproof. Mobile, distributed sensors have been proposed to detect nuclear materials in transit should portal monitors fail to prevent their entry in the first place. In large metropolitan areas, a mobile distributed sensor network could be deployed using vehicle platforms such as taxis, Ubers, and Lyfts, which are already connected to communications infrastructure. However, performance and coverage that could be achieved using a network of sensors mounted on commercial passenger vehicles has not been established. Here, we evaluate how a mobile sensor network could perform in New York City using a combination of radiation transport and geographic information systems. The geographic information system is used in conjunction with OpenStreetMap data to isolate roads and construct a grid over the streets. Vehicle paths are built using pickup and drop off data from Uber, and from the New York State Department of Transportation. The results show that the time to first detection increases with source velocity, decreases with the number of mobile detectors, and reaches a plateau that depends on the strength of the source.

Detecting Unauthorized Movement of Radioactive Material Packages in Transport with an Adam-Optimized BP Neural Network Model

“…A network of mobile distributed sensors was deployed on a vehicle platform to detect mobile radioactive sources. It was found that the detection time of mobile radioactive sources was afected by the speed of the source and the number of mobile detectors [18]. Studies [19] demonstrated that the fusion of extensive data generated by radiation detectors employing Pearson's methodology signifcantly enhances the source detection efectiveness of mobile radioactive materials.…”

“…Terefore, μ and σ 2 can be calculated by introducing the measured RSSI values into equations (17) and (18). To compute the Gaussian distribution corresponding to each set of RSSI, the obtained μ and σ 2 are applied to the density function of the Gaussian distribution, as defned in equation (16).…”

Implementation of Novel Evolutional Algorithm for 3-Dimensional Radiation Mapping and Gamma-Field Reconstruction within the Chornobyl Sarcophagus

“…Although technologies have seen continued advancement over recent decades, following the March 2011 accident at Japan's Fukushima Daiichi Nuclear Power Plant (FDNPP)notable advancements in the unmanned aerial vehicle (UAV) [9][10][11], unmanned ground vehicle (UGV) [12,13], and static/mobile distributed detection systems [14] were realized. Even years after this driver, progress continues across radiation detection, localization, and mapping, whether in underpinning detector materials research (e.g., novel plastics, high-dose semiconductors, dual gammaneutron scintillators) [15][16][17][18]; innovative, autonomous, and miniaturized deployment mechanisms [19][20][21]; or sensor-fusion/data visualization methodologies, progressing from 2D to 3D scenarios [22][23][24][25][26][27]. This research is not just occurring at a small number of institutions and laboratories, but it is a promising and increasingly cross-disciplinary area of active research around the world, applicable for nuclear plant decommissioning, nuclear security, and safeguards applications, whether or not in accident response [25].…”

Real-Time Gamma Radioactive Source Localization by Data Fusion of 3D-LiDAR Terrain Scan and Radiation Data from Semi-Autonomous UAV Flights

“…The use of mobile systems should be mentioned as an alternative. Flanagan et al [ 16 ] propose the use of a mobile distributed sensor network using a public transportation system to protect large metropolitan areas from nuclear threats.…”