{"id":{"repo_id":"uiuc","oai_identifier":"oai:www.ideals.illinois.edu:2142/46685"},"canonical_url":"https://search.dev.ndltd.org/etd/uiuc/oai:www.ideals.illinois.edu:2142/46685","repository":{"repo_id":"uiuc","name":"University of Illinois - Urbana-Champaign","base_url":"https://www.ideals.illinois.edu/oai-pmh"},"display":{"title":"Resource efficient information integration in cyber-physical systems","abstract":"The proliferation of increasingly capable and affordable sensing devices that pervade every corner of the world has given rise to the fast development and wide deployment of Cyber-Physical Systems (CPS). Hosting a whole spectrum of civilian and military applications, cyber-physical systems have fundamentally changed people's ways of everyday living, working, and interactions with the physical world in general. Despite their tremendous benefits, cyber-physical systems pose great new research challenges, of which, this thesis targets on one important facet, that is, to understand and optimize the tradeoff between the quality of information (QoI) provided by the sensor nodes and the consumption of system resources. On one hand, individual sensors are not reliable, due to various possible reasons including incomplete observations, environment and circuit board noise, poor sensor quality, lack of sensor calibration, or even intent to deceive. To address this sensor reliability problem, one common approach is to integrate information from multiple sensors that observe the same events, as this will likely cancel out the errors of individual sensors and improve the quality of information. On the other hand, cyber-physical systems usually have limited resources (e.g., energy, bandwidth, storage, time, space, money, devices or even the human labor). Therefore, it is usually prohibitive to collect data from a large number of sensors due to the potential excessive resource consumption. Targeting on the above challenge, in this thesis we present a suite of resource-efficient information integration tools that can intelligently integrate information from distributed sensors so that the highest quality of information can be achieved, under the constraint of system resources. The proposed information integration toolkit bears superior generalizability and flexibility, and thus can be applied to a full spectrum of application domains.","abstract_html":"The proliferation of increasingly capable and affordable sensing devices that pervade every corner of the world has given rise to the fast development and wide deployment of Cyber-Physical Systems (CPS). Hosting a whole spectrum of civilian and military applications, cyber-physical systems have fundamentally changed people's ways of everyday living, working, and interactions with the physical world in general. Despite their tremendous benefits, cyber-physical systems pose great new research challenges, of which, this thesis targets on one important facet, that is, to understand and optimize the tradeoff between the quality of information (QoI) provided by the sensor nodes and the consumption of system resources. On one hand, individual sensors are not reliable, due to various possible reasons including incomplete observations, environment and circuit board noise, poor sensor quality, lack of sensor calibration, or even intent to deceive. To address this sensor reliability problem, one common approach is to integrate information from multiple sensors that observe the same events, as this will likely cancel out the errors of individual sensors and improve the quality of information. On the other hand, cyber-physical systems usually have limited resources (e.g., energy, bandwidth, storage, time, space, money, devices or even the human labor). Therefore, it is usually prohibitive to collect data from a large number of sensors due to the potential excessive resource consumption. Targeting on the above challenge, in this thesis we present a suite of resource-efficient information integration tools that can intelligently integrate information from distributed sensors so that the highest quality of information can be achieved, under the constraint of system resources. The proposed information integration toolkit bears superior generalizability and flexibility, and thus can be applied to a full spectrum of application domains.","abstract_has_math":false,"creators":["Su, Lu"],"institution":"University of Illinois at Urbana-Champaign","degree_name":"Ph.D.","degree_level":"Dissertation","degree_discipline":"Computer Science","degree_department":null,"school":null,"contributors":["Abdelzaher, Tarek F.","Han, Jiawei","Nahrstedt, Klara","Liu, Xue"],"advisors":[],"committee_chairs":[],"committee_members":[],"year":2014,"date_issued":"2014-01-16T17:59:01Z","date_published":"2014-01-16T17:59:01Z","updated_at":"2026-07-22T22:25:36Z","subjects":["Cyber-Physical Systems","Wireless Sensor Networks","Information Integration","Data Aggregation","Decision Aggregation","Quality of Information","System Resources"],"languages":["en"],"rights":["Copyright 2013 Lu Su"],"rights_urls":[],"identifier_entries":[]},"links":{"outbound_url":"http://hdl.handle.net/2142/46685","outbound_label":"Handle","outbound_source":"dc:identifier"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor","label":"Contributor","values":["Abdelzaher, Tarek F.","Han, Jiawei","Nahrstedt, Klara","Liu, Xue"]},{"key":"dc:creator","label":"Author","values":["Su, Lu"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date","label":"Dc Date","values":["2014-01-16T17:59:01Z","2013-12"]},{"key":"dc:type","label":"Dc Type","values":["text"]},{"key":"thesis:degree_discipline","label":"Discipline","values":["Computer Science"]},{"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":["Cyber-Physical Systems","Wireless Sensor Networks","Information Integration","Data Aggregation","Decision Aggregation","Quality of Information","System Resources"]}]},{"id":"language_rights","label":"Language and Rights","entries":[{"key":"dc:language","label":"Dc Language","values":["en"]},{"key":"dc:rights","label":"Dc Rights","values":["Copyright 2013 Lu Su"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier","label":"Identifier","values":["http://hdl.handle.net/2142/46685"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description","label":"Description","values":["The proliferation of increasingly capable and affordable sensing devices that pervade every corner of the world has given rise to the fast development and wide deployment of Cyber-Physical Systems (CPS). Hosting a whole spectrum of civilian and military applications, cyber-physical systems have fundamentally changed people's ways of everyday living, working, and interactions with the physical world in general. Despite their tremendous benefits, cyber-physical systems pose great new research challenges, of which, this thesis targets on one important facet, that is, to understand and optimize the tradeoff between the quality of information (QoI) provided by the sensor nodes and the consumption of system resources. On one hand, individual sensors are not reliable, due to various possible reasons including incomplete observations, environment and circuit board noise, poor sensor quality, lack of sensor calibration, or even intent to deceive. To address this sensor reliability problem, one common approach is to integrate information from multiple sensors that observe the same events, as this will likely cancel out the errors of individual sensors and improve the quality of information. On the other hand, cyber-physical systems usually have limited resources (e.g., energy, bandwidth, storage, time, space, money, devices or even the human labor). Therefore, it is usually prohibitive to collect data from a large number of sensors due to the potential excessive resource consumption. Targeting on the above challenge, in this thesis we present a suite of resource-efficient information integration tools that can intelligently integrate information from distributed sensors so that the highest quality of information can be achieved, under the constraint of system resources. The proposed information integration toolkit bears superior generalizability and flexibility, and thus can be applied to a full spectrum of application domains.","Item withdrawn by Mark Zulauf (zulauf@illinois.edu) on 2013-12-04T14:24:47Z Item was in collections: University of Illinois Theses & Dissertations (ID: 1) No. of bitstreams: 1 Su_Lu.pdf: 1879510 bytes, checksum: 1b985369461abba9ab65df52223f2940 (MD5)","Made available in DSpace on 2014-01-16T17:59:01Z (GMT). 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Despite their tremendous benefits, cyber-physical systems pose great new research challenges, of which, this thesis targets on one important facet, that is, to understand and optimize the tradeoff between the quality of information (QoI) provided by the sensor nodes and the consumption of system resources. On one hand, individual sensors are not reliable, due to various possible reasons including incomplete observations, environment and circuit board noise, poor sensor quality, lack of sensor calibration, or even intent to deceive. To address this sensor reliability problem, one common approach is to integrate information from multiple sensors that observe the same events, as this will likely cancel out the errors of individual sensors and improve the quality of information. On the other hand, cyber-physical systems usually have limited resources (e.g., energy, bandwidth, storage, time, space, money, devices or even the human labor). Therefore, it is usually prohibitive to collect data from a large number of sensors due to the potential excessive resource consumption. Targeting on the above challenge, in this thesis we present a suite of resource-efficient information integration tools that can intelligently integrate information from distributed sensors so that the highest quality of information can be achieved, under the constraint of system resources. The proposed information integration toolkit bears superior generalizability and flexibility, and thus can be applied to a full spectrum of application domains.","Item withdrawn by Mark Zulauf (zulauf@illinois.edu) on 2013-12-04T14:24:47Z Item was in collections: University of Illinois Theses & Dissertations (ID: 1) No. of bitstreams: 1 Su_Lu.pdf: 1879510 bytes, checksum: 1b985369461abba9ab65df52223f2940 (MD5)","Made available in DSpace on 2014-01-16T17:59:01Z (GMT). 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