{"id":{"repo_id":"uiuc","oai_identifier":"oai:www.ideals.illinois.edu:2142/99369"},"canonical_url":"https://search.dev.ndltd.org/etd/uiuc/oai:www.ideals.illinois.edu:2142/99369","repository":{"repo_id":"uiuc","name":"University of Illinois - Urbana-Champaign","base_url":"https://www.ideals.illinois.edu/oai-pmh"},"display":{"title":"Motion tracking problems in Internet of Things (IoT) and wireless networking","abstract":"\"The dissertation focuses on inferring various motion patterns of internet-of-things (IoT) devices, by leveraging inertial sensors embedded in these objects, as well as wireless signals emitted (or reflected) from them. For instance, we use a combination of GPS signals and inertial sensors on drones to precisely track its 3D orientation over time, ultimately improving safety against failures and crashes. In another application in sports analytics, we embed sensors and radios inside baseballs and cricket balls and compute their 3D trajectory and spin patterns, even when they move at extremely high speeds. In a third application for wireless networks, we explore the possibility of physically moving wireless infrastructure like Access Points and basestations on robots and drones for enhancing the network performance. While these are diverse applications in drones, sports analytics, and wireless networks, the common theme underlying the research is in the development of the core motion-related building blocks. Specifically, we emphasize the philosophy of \"\"fusion of multi modal sensor data with application specific model” as the design principle for building the next generation of diverse IoT applications. To this end, we draw on theoretical techniques in wireless communication, signal processing, and statistics, but translate them to completely functional systems on real-world platforms.\"","abstract_html":""The dissertation focuses on inferring various motion patterns of internet-of-things (IoT) devices, by leveraging inertial sensors embedded in these objects, as well as wireless signals emitted (or reflected) from them. For instance, we use a combination of GPS signals and inertial sensors on drones to precisely track its 3D orientation over time, ultimately improving safety against failures and crashes. In another application in sports analytics, we embed sensors and radios inside baseballs and cricket balls and compute their 3D trajectory and spin patterns, even when they move at extremely high speeds. In a third application for wireless networks, we explore the possibility of physically moving wireless infrastructure like Access Points and basestations on robots and drones for enhancing the network performance. While these are diverse applications in drones, sports analytics, and wireless networks, the common theme underlying the research is in the development of the core motion-related building blocks. Specifically, we emphasize the philosophy of ""fusion of multi modal sensor data with application specific model” as the design principle for building the next generation of diverse IoT applications. To this end, we draw on theoretical techniques in wireless communication, signal processing, and statistics, but translate them to completely functional systems on real-world platforms."","abstract_has_math":false,"creators":["Gowda, Mahanth Krishnappa"],"institution":"University of Illinois at Urbana-Champaign","degree_name":"Ph.D.","degree_level":"Dissertation","degree_discipline":"Computer Science","degree_department":null,"school":null,"contributors":["Choudhury, Romit Roy","Maggs, Bruce","Vaidya, Nitin","Nahrstedt, Klara","Abdelzaher, Tarek"],"advisors":[],"committee_chairs":[],"committee_members":[],"year":2018,"date_issued":"2018-03-13T15:48:56Z","date_published":"2018-03-13T15:48:56Z","updated_at":"2026-07-22T22:24:37Z","subjects":["Differential Global Positioning System (GPS)","Carrier phases","Unmanned aerial vehicle (UAV)","Drones","Inertial measurement units (IMU)","Wireless","Robotic networks","Infrastructure","Measurement","Wearables","Analytics","Ultra-wideband (UWB)","Sports","Internet of Things (IoT)","Embedded sensing"],"languages":["en"],"rights":["Copyright 2017 Mahanth Krishnappa Gowda"],"rights_urls":[],"identifier_entries":[]},"links":{"outbound_url":"http://hdl.handle.net/2142/99369","outbound_label":"Handle","outbound_source":"dc:identifier"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor","label":"Contributor","values":["Choudhury, Romit Roy","Maggs, Bruce","Vaidya, Nitin","Nahrstedt, Klara","Abdelzaher, Tarek"]},{"key":"dc:creator","label":"Author","values":["Gowda, Mahanth Krishnappa"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date","label":"Dc Date","values":["2018-03-13T15:48:56Z","2017-12-05","2017-12"]},{"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":["Differential Global Positioning System (GPS)","Carrier phases","Unmanned aerial vehicle (UAV)","Drones","Inertial measurement units (IMU)","Wireless","Robotic networks","Infrastructure","Measurement","Wearables","Analytics","Ultra-wideband (UWB)","Sports","Internet of Things (IoT)","Embedded sensing"]}]},{"id":"language_rights","label":"Language and Rights","entries":[{"key":"dc:language","label":"Dc Language","values":["en"]},{"key":"dc:rights","label":"Dc Rights","values":["Copyright 2017 Mahanth Krishnappa Gowda"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier","label":"Identifier","values":["http://hdl.handle.net/2142/99369"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description","label":"Description","values":["\"The dissertation focuses on inferring various motion patterns of internet-of-things (IoT) devices, by leveraging inertial sensors embedded in these objects, as well as wireless signals emitted (or reflected) from them. For instance, we use a combination of GPS signals and inertial sensors on drones to precisely track its 3D orientation over time, ultimately improving safety against failures and crashes. In another application in sports analytics, we embed sensors and radios inside baseballs and cricket balls and compute their 3D trajectory and spin patterns, even when they move at extremely high speeds. In a third application for wireless networks, we explore the possibility of physically moving wireless infrastructure like Access Points and basestations on robots and drones for enhancing the network performance. While these are diverse applications in drones, sports analytics, and wireless networks, the common theme underlying the research is in the development of the core motion-related building blocks. Specifically, we emphasize the philosophy of \"\"fusion of multi modal sensor data with application specific model” as the design principle for building the next generation of diverse IoT applications. To this end, we draw on theoretical techniques in wireless communication, signal processing, and statistics, but translate them to completely functional systems on real-world platforms.\"","Submission original under an indefinite embargo labeled 'Open Access'. The submission was exported from vireo on 2018-03-13 without embargo terms","The student, Mahanth Krishnappa Gowda, accepted the attached license on 2017-12-04 at 21:50.","The student, Mahanth Krishnappa Gowda, submitted this Dissertation for approval on 2017-12-04 at 22:06.","This Dissertation was approved for publication on 2017-12-05 at 10:23.","DSpace SAF Submission Ingestion Package generated from Vireo submission #11848 on 2018-03-13 at 10:10:35","Made available in DSpace on 2018-03-13T15:48:56Z (GMT). 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For instance, we use a combination of GPS signals and inertial sensors on drones to precisely track its 3D orientation over time, ultimately improving safety against failures and crashes. In another application in sports analytics, we embed sensors and radios inside baseballs and cricket balls and compute their 3D trajectory and spin patterns, even when they move at extremely high speeds. In a third application for wireless networks, we explore the possibility of physically moving wireless infrastructure like Access Points and basestations on robots and drones for enhancing the network performance. While these are diverse applications in drones, sports analytics, and wireless networks, the common theme underlying the research is in the development of the core motion-related building blocks. 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