{"id":{"repo_id":"uiuc","oai_identifier":"oai:www.ideals.illinois.edu:2142/106386"},"canonical_url":"https://search.dev.ndltd.org/etd/uiuc/oai:www.ideals.illinois.edu:2142/106386","repository":{"repo_id":"uiuc","name":"University of Illinois - Urbana-Champaign","base_url":"https://www.ideals.illinois.edu/oai-pmh"},"display":{"title":"A comparison of geostatistcal interpolation methods for the characterization of background radiation collected on a mobile sensor network in a cloud computing environment","abstract":"There is a need to identify illicit radioactive sources in urban environments to prevent potential national security crises. These illicit materials can be the precursors to dirty bombs as well as broader nuclear proliferation. Mobile Sensor Networks deployed in major metropolitan areas have been proposed to address this security and proliferation challenge. However, these networks generate large amounts of data that cannot be processed on stand-alone computers. This volume of data requires the application of novel Big Data Analytics tools and techniques. Additionally, the presence of permanent human-made background radiation sources like buildings and monuments and natural fluctuations in background radiation can make detecting illicit radioactive sources with methods relying on simple radiation thresholds challenging. If the background radiation is well characterized, then more precise thresholds can be implemented to lower the rate of false alarms. The background radiation data is often sparse, and interpolation is needed to arrive at well characterized data. The performance of two methods of geospatial interpolation, Inverse Distance Weighting and Kriging, on background radiation in a cloud computing environment are evaluated in this thesis, and recommendations on data collection and interpolation methods for ideal background characterization are provided.","abstract_html":"There is a need to identify illicit radioactive sources in urban environments to prevent potential national security crises. These illicit materials can be the precursors to dirty bombs as well as broader nuclear proliferation. Mobile Sensor Networks deployed in major metropolitan areas have been proposed to address this security and proliferation challenge. However, these networks generate large amounts of data that cannot be processed on stand-alone computers. This volume of data requires the application of novel Big Data Analytics tools and techniques. Additionally, the presence of permanent human-made background radiation sources like buildings and monuments and natural fluctuations in background radiation can make detecting illicit radioactive sources with methods relying on simple radiation thresholds challenging. If the background radiation is well characterized, then more precise thresholds can be implemented to lower the rate of false alarms. The background radiation data is often sparse, and interpolation is needed to arrive at well characterized data. The performance of two methods of geospatial interpolation, Inverse Distance Weighting and Kriging, on background radiation in a cloud computing environment are evaluated in this thesis, and recommendations on data collection and interpolation methods for ideal background characterization are provided.","abstract_has_math":false,"creators":["Roth, Naomi"],"institution":"University of Illinois at Urbana-Champaign","degree_name":"M.S.","degree_level":"Thesis","degree_discipline":"Nuclear, Plasma, and Radiological Engineering","degree_department":null,"school":null,"contributors":["Uddin, Rizwan","Huff, Kathryn D"],"advisors":[],"committee_chairs":[],"committee_members":[],"year":2020,"date_issued":"2020-03-02T22:15:16Z","date_published":"2020-03-02T22:15:16Z","updated_at":"2026-07-22T22:24:45Z","subjects":["Nuclear Security","geospatial","sensor network","gis","nonproliferation","radiation","natural resources","Kriging","Inverse Distance Weighting","Big Data","Spark","Hadoop"],"languages":["en"],"rights":["Copyright 2019 Naomi Roth"],"rights_urls":[],"identifier_entries":[]},"links":{"outbound_url":"http://hdl.handle.net/2142/106386","outbound_label":"Handle","outbound_source":"dc:identifier"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor","label":"Contributor","values":["Uddin, Rizwan","Huff, Kathryn D"]},{"key":"dc:creator","label":"Author","values":["Roth, Naomi"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date","label":"Dc Date","values":["2020-03-02T22:15:16Z","2022-03-03T10:15:16Z","2019-12-11","2019-12"]},{"key":"dc:type","label":"Dc Type","values":["text"]},{"key":"thesis:degree_discipline","label":"Discipline","values":["Nuclear, Plasma, and Radiological Engineering"]},{"key":"thesis:degree_level","label":"Degree Level","values":["Thesis"]},{"key":"thesis:degree_name","label":"Degree Name","values":["M.S."]},{"key":"thesis:institution_name","label":"Thesis Institution Name","values":["University of Illinois at Urbana-Champaign"]}]},{"id":"subjects_keywords","label":"Subjects and Keywords","entries":[{"key":"dc:subject","label":"Dc Subject","values":["Nuclear Security","geospatial","sensor network","gis","nonproliferation","radiation","natural resources","Kriging","Inverse Distance Weighting","Big Data","Spark","Hadoop"]}]},{"id":"language_rights","label":"Language and Rights","entries":[{"key":"dc:language","label":"Dc Language","values":["en"]},{"key":"dc:rights","label":"Dc Rights","values":["Copyright 2019 Naomi Roth"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier","label":"Identifier","values":["http://hdl.handle.net/2142/106386"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description","label":"Description","values":["There is a need to identify illicit radioactive sources in urban environments to prevent potential national security crises. These illicit materials can be the precursors to dirty bombs as well as broader nuclear proliferation. Mobile Sensor Networks deployed in major metropolitan areas have been proposed to address this security and proliferation challenge. However, these networks generate large amounts of data that cannot be processed on stand-alone computers. This volume of data requires the application of novel Big Data Analytics tools and techniques. Additionally, the presence of permanent human-made background radiation sources like buildings and monuments and natural fluctuations in background radiation can make detecting illicit radioactive sources with methods relying on simple radiation thresholds challenging. If the background radiation is well characterized, then more precise thresholds can be implemented to lower the rate of false alarms. The background radiation data is often sparse, and interpolation is needed to arrive at well characterized data. The performance of two methods of geospatial interpolation, Inverse Distance Weighting and Kriging, on background radiation in a cloud computing environment are evaluated in this thesis, and recommendations on data collection and interpolation methods for ideal background characterization are provided.","Submission published under a 24 month embargo labeled 'U of I Access', the embargo will last until 2021-12-01","The student, Naomi Roth, accepted the attached license on 2019-12-09 at 20:43.","The student, Naomi Roth, submitted this Thesis for approval on 2019-12-09 at 20:50.","This Thesis was approved for publication on 2019-12-11 at 16:05.","DSpace SAF Submission Ingestion Package generated from Vireo submission #14759 on 2020-02-28 at 17:23:57","Made available in DSpace on 2020-03-02T22:15:16Z (GMT). No. of bitstreams: 3 ROTH-THESIS-2019.pdf: 11468210 bytes, checksum: 0d03fea536202298a1cf00c94f370cbe (MD5) main.tex: 6324 bytes, checksum: 36ce3401a679b9e398d2063194f0962c (MD5) LICENSE.txt: 4206 bytes, checksum: b3983e52bb1a2ea398a417074ccfe7f2 (MD5) Previous issue date: 2019-12-11","Embargo set by: Seth Robbins for item 113928 Lift date: 2022-03-02T22:15:21Z Reason: Author requested U of Illinois access only (OA after 2yrs) in Vireo ETD system","Embargo set by: Seth Robbins for item 113928 Lift date: 2022-03-02T22:18:25Z Reason: Author requested U of Illinois access only (OA after 2yrs) in Vireo ETD system","U of I Only Restriction Lifted for Item 113928 on 2022-03-03T10:15:16Z."]},{"key":"dc:format","label":"Dc Format","values":["application/pdf"]},{"key":"dc:title","label":"Title","values":["A comparison of geostatistcal interpolation methods for the characterization of background radiation collected on a mobile sensor network in a cloud computing environment"]}]}],"canonical_facts":{"dc:contributor":["Uddin, Rizwan","Huff, Kathryn D"],"dc:creator":["Roth, Naomi"],"dc:date":["2020-03-02T22:15:16Z","2022-03-03T10:15:16Z","2019-12-11","2019-12"],"dc:description":["There is a need to identify illicit radioactive sources in urban environments to prevent potential national security crises. These illicit materials can be the precursors to dirty bombs as well as broader nuclear proliferation. Mobile Sensor Networks deployed in major metropolitan areas have been proposed to address this security and proliferation challenge. However, these networks generate large amounts of data that cannot be processed on stand-alone computers. This volume of data requires the application of novel Big Data Analytics tools and techniques. Additionally, the presence of permanent human-made background radiation sources like buildings and monuments and natural fluctuations in background radiation can make detecting illicit radioactive sources with methods relying on simple radiation thresholds challenging. If the background radiation is well characterized, then more precise thresholds can be implemented to lower the rate of false alarms. The background radiation data is often sparse, and interpolation is needed to arrive at well characterized data. The performance of two methods of geospatial interpolation, Inverse Distance Weighting and Kriging, on background radiation in a cloud computing environment are evaluated in this thesis, and recommendations on data collection and interpolation methods for ideal background characterization are provided.","Submission published under a 24 month embargo labeled 'U of I Access', the embargo will last until 2021-12-01","The student, Naomi Roth, accepted the attached license on 2019-12-09 at 20:43.","The student, Naomi Roth, submitted this Thesis for approval on 2019-12-09 at 20:50.","This Thesis was approved for publication on 2019-12-11 at 16:05.","DSpace SAF Submission Ingestion Package generated from Vireo submission #14759 on 2020-02-28 at 17:23:57","Made available in DSpace on 2020-03-02T22:15:16Z (GMT). No. of bitstreams: 3 ROTH-THESIS-2019.pdf: 11468210 bytes, checksum: 0d03fea536202298a1cf00c94f370cbe (MD5) main.tex: 6324 bytes, checksum: 36ce3401a679b9e398d2063194f0962c (MD5) LICENSE.txt: 4206 bytes, checksum: b3983e52bb1a2ea398a417074ccfe7f2 (MD5) Previous issue date: 2019-12-11","Embargo set by: Seth Robbins for item 113928 Lift date: 2022-03-02T22:15:21Z Reason: Author requested U of Illinois access only (OA after 2yrs) in Vireo ETD system","Embargo set by: Seth Robbins for item 113928 Lift date: 2022-03-02T22:18:25Z Reason: Author requested U of Illinois access only (OA after 2yrs) in Vireo ETD system","U of I Only Restriction Lifted for Item 113928 on 2022-03-03T10:15:16Z."],"dc:format":["application/pdf"],"dc:identifier":["http://hdl.handle.net/2142/106386"],"dc:language":["en"],"dc:rights":["Copyright 2019 Naomi Roth"],"dc:subject":["Nuclear Security","geospatial","sensor network","gis","nonproliferation","radiation","natural resources","Kriging","Inverse Distance Weighting","Big Data","Spark","Hadoop"],"dc:title":["A comparison of geostatistcal interpolation methods for the characterization of background radiation collected on a mobile sensor network in a cloud computing environment"],"dc:type":["text"],"thesis:degree_discipline":["Nuclear, Plasma, and Radiological Engineering"],"thesis:degree_level":["Thesis"],"thesis:degree_name":["M.S."],"thesis:institution_name":["University of Illinois at Urbana-Champaign"]},"updated_at":"2026-07-22T22:24:45Z"}