2026-09-20T16:24:52Z https://www.ideals.illinois.edu/oai-pmh

oai:www.ideals.illinois.edu:2142/97423 2023-07-11 col_2142_5131 col_2142_14837 com_2142_5130 com_2142_14836 com_2142_234

Sullivan, Clair J. Huff, Kathryn D. Cheng, Michael 2017-08-10T19:15:39Z 2017-08-10T19:15:39Z 2017-04-26 2017-05 Mobile sensor networks offer unique advantages over their stationary counterparts in the area of anomalous radioactive source localization. Being mobile, the same number of sensors are capable of covering a much larger area than an equivalent number of stationary sensors. We have developed and deployed a mobile sensor network comprised of Kromek D3S gamma ray and thermal neutron detectors paired via Bluetooth with Samsung Galaxy S6 smartphones. An Android app was written that allows for communication between the phone and detector, allowing for radiation data to be queried, geo-taged, timestamped, and sent to an off-site repository in the cloud for storage and data analysis. AWS was selected to be the cloud platform to support this sensor network as it offered highly modular and affordable data storage and computational services. Three sources of location data, GPS, WiFi, and cell tower triangulation were evaluated to determine optimal position accuracy. GPS proved to be the most accurate in an outdoor environment with clear skies, being able to achieve an accuracy of 3m in tests, but performed significantly worse in indoor environments or near buildings and other structures. WiFi was found to be heavily dependent upon the proximity of WiFi access points near the phone, and cell tower triangulation was deemed unusable for source localization due to the low number density of cell towers. Application of Kalman filtering to both indoor and outdoor location data yielded mixed results. Submission original under an indefinite embargo labeled 'Open Access'. The submission was exported from vireo on 2017-08-10 without embargo terms The student, Michael Cheng, accepted the attached license on 2017-04-20 at 14:59. The student, Michael Cheng, submitted this Thesis for approval on 2017-04-24 at 13:53. This Thesis was approved for publication on 2017-04-26 at 17:39. DSpace SAF Submission Ingestion Package generated from Vireo submission #10940 on 2017-08-10 at 13:43:33 Made available in DSpace on 2017-08-10T19:15:39Z (GMT). No. of bitstreams: 2 CHENG-THESIS-2017.pdf: 6675792 bytes, checksum: 179b4d8fa0a94fd2a43e11769e1a1d89 (MD5) LICENSE.txt: 4210 bytes, checksum: af623882dfc5d5917c47c5260194d7f1 (MD5) Previous issue date: 2017-04-26 application/pdf http://hdl.handle.net/2142/97423 en Copyright 2017 Michael Cheng Mobile sensor network Radiation detection Nuclear nonproliferation Participatory sensing Analysis of a custom android app designed to utilize cloud infrastructure in support of mobile sensor networks text text Nuclear, Plasma, & Rad Engr Nuclear, Plasma, Radiolgc Engr University of Illinois at Urbana-Champaign Thesis M.S.