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Title:Analysis of a custom android app designed to utilize cloud infrastructure in support of mobile sensor networks
Author(s):Cheng, Michael
Advisor(s):Sullivan, Clair J
Contributor(s):Huff, Kathryn D
Department / Program:Nuclear, Plasma, & Rad Engr
Discipline:Nuclear, Plasma, Radiolgc Engr
Degree Granting Institution:University of Illinois at Urbana-Champaign
Degree:M.S.
Genre:Thesis
Subject(s):Mobile sensor network
Radiation detection
Nuclear nonproliferation
Participatory sensing
Abstract: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.
Issue Date:2017-04-26
Type:Text
URI:http://hdl.handle.net/2142/97423
Rights Information:Copyright 2017 Michael Cheng
Date Available in IDEALS:2017-08-10
Date Deposited:2017-05


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