Issue |
EPJ Web Conf.
Volume 247, 2021
PHYSOR2020 – International Conference on Physics of Reactors: Transition to a Scalable Nuclear Future
|
|
---|---|---|
Article Number | 16003 | |
Number of page(s) | 8 | |
Section | Radiation Applications / Nuclear Safeguards | |
DOI | https://doi.org/10.1051/epjconf/202124716003 | |
Published online | 22 February 2021 |
https://doi.org/10.1051/epjconf/202124716003
DETECTING NUCLEAR MATERIALS IN URBAN ENVIRONMENTS USING MOBILE SENSOR NETWORKS
1 Nuclear Science and Engineering, Colorado School of Mines, 1500 Illinois St. Golden, CO 80401
2 United States Air Force Academy, Colorado Springs 80840-5002
* mdeinert@mines.edu
osbornea@mines.edu
Published online: 22 February 2021
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 fool proof. 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, the potential geographic 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 QGIS 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 data from the New York State Department of Transportation.
Key words: distributed sensor network
© The Authors, published by EDP Sciences, 2021
This is an Open Access article distributed under the terms of the Creative Commons Attribution License 4.0, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Current usage metrics show cumulative count of Article Views (full-text article views including HTML views, PDF and ePub downloads, according to the available data) and Abstracts Views on Vision4Press platform.
Data correspond to usage on the plateform after 2015. The current usage metrics is available 48-96 hours after online publication and is updated daily on week days.
Initial download of the metrics may take a while.