{"id":{"repo_id":"uiuc","oai_identifier":"oai:www.ideals.illinois.edu:2142/105205"},"canonical_url":"https://search.dev.ndltd.org/etd/uiuc/oai:www.ideals.illinois.edu:2142/105205","repository":{"repo_id":"uiuc","name":"University of Illinois - Urbana-Champaign","base_url":"https://www.ideals.illinois.edu/oai-pmh"},"display":{"title":"Implementation and simulation of mobile sensor networks for nuclear radiation detection","abstract":"From preventing the threat of nuclear weapons proliferation to monitoring the transportation of special nuclear materials, nuclear radiation detection plays an important role in national security applications. However, changing background radiation, shielding effects, and short collection time make radiation detection a challenging problem. Anomaly detection, source localization, and isotope identification are three major parts of radiation detection. The concept of mobile radiation sensor networks, which utilize multiple mobile radiation detectors, has been proposed to solve these problems. This work mainly focuses on developing and testing methodologies for anomaly detection and radioactive source localization using mobile sensor networks. A collection of techniques and analyses for radiation detection are presented and evaluated. More specifically, in this work, a mobile sensor network simulation system is first developed to simulate the scenario where multiple radiation detectors move around a city. Based on the simulated data, the performance characteristics of mobile sensor networks for radiation detection are studied and quantified. Next, focusing on geospatial modeling of radiation count data, Poisson kriging is proposed to estimate the background radiation level and perform anomalous source detection. The proposed method is validated using simulated source data injected in measured background radiation data and results indicate that the proposed algorithm can detect the anomalous radiation source with 90% accuracy under certain conditions. Additionally, source localization techniques based on maximum likelihood estimation are explored in detail. Simulation and experimental results show that source localization error can be reduced to be within 3 meters. Lastly, an exploratory study of spectrum-based anomaly detection techniques is presented. The performance of different machine learning techniques is evaluated and compared using simulated radiation data.","abstract_html":"From preventing the threat of nuclear weapons proliferation to monitoring the transportation of special nuclear materials, nuclear radiation detection plays an important role in national security applications. However, changing background radiation, shielding effects, and short collection time make radiation detection a challenging problem. Anomaly detection, source localization, and isotope identification are three major parts of radiation detection. The concept of mobile radiation sensor networks, which utilize multiple mobile radiation detectors, has been proposed to solve these problems. This work mainly focuses on developing and testing methodologies for anomaly detection and radioactive source localization using mobile sensor networks. A collection of techniques and analyses for radiation detection are presented and evaluated. More specifically, in this work, a mobile sensor network simulation system is first developed to simulate the scenario where multiple radiation detectors move around a city. Based on the simulated data, the performance characteristics of mobile sensor networks for radiation detection are studied and quantified. Next, focusing on geospatial modeling of radiation count data, Poisson kriging is proposed to estimate the background radiation level and perform anomalous source detection. The proposed method is validated using simulated source data injected in measured background radiation data and results indicate that the proposed algorithm can detect the anomalous radiation source with 90% accuracy under certain conditions. Additionally, source localization techniques based on maximum likelihood estimation are explored in detail. Simulation and experimental results show that source localization error can be reduced to be within 3 meters. Lastly, an exploratory study of spectrum-based anomaly detection techniques is presented. The performance of different machine learning techniques is evaluated and compared using simulated radiation data.","abstract_has_math":false,"creators":["Zhao, Jifu"],"institution":"University of Illinois at Urbana-Champaign","degree_name":"Ph.D.","degree_level":"Dissertation","degree_discipline":"Nuclear, Plasma, Radiolgc Engr","degree_department":null,"school":null,"contributors":["Uddin, Rizwan","Abbaszadeh, Shiva","Brunner, Robert J.","Huff, Kathryn D.","Sullivan, Clair J."],"advisors":[],"committee_chairs":[],"committee_members":[],"year":2019,"date_issued":"2019-08-23T20:47:29Z","date_published":"2019-08-23T20:47:29Z","updated_at":"2026-07-22T22:24:44Z","subjects":["Nuclear radiation detection","Mobile sensor network","Anomaly detection","Source localization"],"languages":["en"],"rights":["Copyright 2019 Jifu Zhao"],"rights_urls":[],"identifier_entries":[]},"links":{"outbound_url":"http://hdl.handle.net/2142/105205","outbound_label":"Handle","outbound_source":"dc:identifier"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor","label":"Contributor","values":["Uddin, Rizwan","Abbaszadeh, Shiva","Brunner, Robert J.","Huff, Kathryn D.","Sullivan, Clair J."]},{"key":"dc:creator","label":"Author","values":["Zhao, Jifu"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date","label":"Dc Date","values":["2019-08-23T20:47:29Z","2021-08-24T09:15:28Z","2019-04-17","2019-05"]},{"key":"dc:type","label":"Dc Type","values":["text"]},{"key":"thesis:degree_discipline","label":"Discipline","values":["Nuclear, Plasma, Radiolgc Engr"]},{"key":"thesis:degree_level","label":"Degree Level","values":["Dissertation"]},{"key":"thesis:degree_name","label":"Degree Name","values":["Ph.D."]},{"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 radiation detection","Mobile sensor network","Anomaly detection","Source localization"]}]},{"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 Jifu Zhao"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier","label":"Identifier","values":["http://hdl.handle.net/2142/105205"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description","label":"Description","values":["From preventing the threat of nuclear weapons proliferation to monitoring the transportation of special nuclear materials, nuclear radiation detection plays an important role in national security applications. However, changing background radiation, shielding effects, and short collection time make radiation detection a challenging problem. Anomaly detection, source localization, and isotope identification are three major parts of radiation detection. The concept of mobile radiation sensor networks, which utilize multiple mobile radiation detectors, has been proposed to solve these problems. This work mainly focuses on developing and testing methodologies for anomaly detection and radioactive source localization using mobile sensor networks. A collection of techniques and analyses for radiation detection are presented and evaluated. More specifically, in this work, a mobile sensor network simulation system is first developed to simulate the scenario where multiple radiation detectors move around a city. Based on the simulated data, the performance characteristics of mobile sensor networks for radiation detection are studied and quantified. Next, focusing on geospatial modeling of radiation count data, Poisson kriging is proposed to estimate the background radiation level and perform anomalous source detection. The proposed method is validated using simulated source data injected in measured background radiation data and results indicate that the proposed algorithm can detect the anomalous radiation source with 90% accuracy under certain conditions. Additionally, source localization techniques based on maximum likelihood estimation are explored in detail. Simulation and experimental results show that source localization error can be reduced to be within 3 meters. Lastly, an exploratory study of spectrum-based anomaly detection techniques is presented. The performance of different machine learning techniques is evaluated and compared using simulated radiation data.","Submission published under a 24 month embargo labeled 'Closed Access', the embargo will last until 2021-05-01","The student, Jifu Zhao, accepted the attached license on 2019-04-16 at 11:25.","The student, Jifu Zhao, submitted this Dissertation for approval on 2019-04-16 at 11:27.","This Dissertation was approved for publication on 2019-04-17 at 11:08.","DSpace SAF Submission Ingestion Package generated from Vireo submission #13670 on 2019-08-22 at 16:22:17","Made available in DSpace on 2019-08-23T20:47:29Z (GMT). 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However, changing background radiation, shielding effects, and short collection time make radiation detection a challenging problem. Anomaly detection, source localization, and isotope identification are three major parts of radiation detection. The concept of mobile radiation sensor networks, which utilize multiple mobile radiation detectors, has been proposed to solve these problems. This work mainly focuses on developing and testing methodologies for anomaly detection and radioactive source localization using mobile sensor networks. A collection of techniques and analyses for radiation detection are presented and evaluated. More specifically, in this work, a mobile sensor network simulation system is first developed to simulate the scenario where multiple radiation detectors move around a city. Based on the simulated data, the performance characteristics of mobile sensor networks for radiation detection are studied and quantified. Next, focusing on geospatial modeling of radiation count data, Poisson kriging is proposed to estimate the background radiation level and perform anomalous source detection. The proposed method is validated using simulated source data injected in measured background radiation data and results indicate that the proposed algorithm can detect the anomalous radiation source with 90% accuracy under certain conditions. Additionally, source localization techniques based on maximum likelihood estimation are explored in detail. Simulation and experimental results show that source localization error can be reduced to be within 3 meters. Lastly, an exploratory study of spectrum-based anomaly detection techniques is presented. The performance of different machine learning techniques is evaluated and compared using simulated radiation data.","Submission published under a 24 month embargo labeled 'Closed Access', the embargo will last until 2021-05-01","The student, Jifu Zhao, accepted the attached license on 2019-04-16 at 11:25.","The student, Jifu Zhao, submitted this Dissertation for approval on 2019-04-16 at 11:27.","This Dissertation was approved for publication on 2019-04-17 at 11:08.","DSpace SAF Submission Ingestion Package generated from Vireo submission #13670 on 2019-08-22 at 16:22:17","Made available in DSpace on 2019-08-23T20:47:29Z (GMT). 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