{"id":{"repo_id":"uiuc","oai_identifier":"oai:www.ideals.illinois.edu:2142/127254"},"canonical_url":"https://search.dev.ndltd.org/etd/uiuc/oai:www.ideals.illinois.edu:2142/127254","repository":{"repo_id":"uiuc","name":"University of Illinois - Urbana-Champaign","base_url":"https://www.ideals.illinois.edu/oai-pmh"},"display":{"title":"Generation models for internet of things sensing applications","abstract":"Submission original under an indefinite embargo labeled 'Open Access'. The submission was exported from vireo on 2025-03-28 without embargo terms","abstract_html":"Submission original under an indefinite embargo labeled 'Open Access'. The submission was exported from vireo on 2025-03-28 without embargo terms","abstract_has_math":false,"creators":["Wang, Tianshi"],"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","Nahrstedt, Klara","Zhao, Han","Srivastava, Mani"],"advisors":[],"committee_chairs":[],"committee_members":[],"year":2024,"date_issued":"2024-12","date_published":"2024-12","updated_at":"2026-07-22T22:25:03Z","subjects":["Internet Of Things","Generation Models","Deep Learning"],"languages":["en","eng"],"rights":["Copyright 2024 Tianshi Wang"],"rights_urls":[],"identifier_entries":[]},"links":{"outbound_url":"https://hdl.handle.net/2142/127254","outbound_label":"Handle","outbound_source":"dc:identifier"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor","label":"Contributor","values":["Abdelzaher, Tarek","Nahrstedt, Klara","Zhao, Han","Srivastava, Mani"]},{"key":"dc:creator","label":"Author","values":["Wang, Tianshi"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date","label":"Dc Date","values":["2024-12","2024-12-03"]},{"key":"dc:type","label":"Dc Type","values":["text","Thesis"]},{"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":["Internet Of Things","Generation Models","Deep Learning"]}]},{"id":"language_rights","label":"Language and Rights","entries":[{"key":"dc:language","label":"Dc Language","values":["en","eng"]},{"key":"dc:rights","label":"Dc Rights","values":["Copyright 2024 Tianshi Wang"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier","label":"Identifier","values":["https://hdl.handle.net/2142/127254"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description","label":"Description","values":["Submission original under an indefinite embargo labeled 'Open Access'. The submission was exported from vireo on 2025-03-28 without embargo terms","The student, Tianshi Wang, accepted the attached license on 2024-12-02 at 18:02.","The student, Tianshi Wang, submitted this Dissertation for approval on 2024-12-02 at 18:16.","This Dissertation was approved for publication on 2024-12-03 at 14:59.","DSpace SAF Submission Ingestion Package generated from Vireo submission #21456 on 2025-03-28 at 14:27:49","The widespread deployment of Internet of Things (IoT) sensors has transformed how we observe physical phenomena and integrate computation into everyday life. However, despite the vast amount of data generated by these sensors daily, there remains a critical need for high-quality, task-specific datasets. As deep learning models increasingly demand larger volumes of data, the artificial generation of data for IoT sensing applications has become an essential research area. This dissertation presents a comprehensive exploration of generation models for synthesizing IoT sensing signals. Through seven chapters, it investigates the design, innovation, and practical application of various generative models in addressing key challenges in IoT data generation. Chapter 1 sets the stage by discussing the key challenges motivating the need for generation approaches in IoT sensing and outlines the scope. Chapter 2 demonstrates the effectiveness of discriminative models in generating signals with robust input-output mappings, exemplified by a task that transforms motion sensor signals into human speech audio. Chapter 3 delves into the strength of VAEs in disentangling the factors that influence data generation, highlighting a data augmentation framework for IoT applications as a proof of concept. In Chapter 4, diffusion models are explored, showcasing their capability to generate high-quality data in a vehicle detection scenario. Chapter 5 investigates the condition space of conditional generative models and the potential to manipulate this space for controlled data synthesis. Chapter 6 extends this exploration by proposing methodologies for achieving fine-grained control over the IoT sensing data generation process. Chapter 7 concludes this dissertation. This work advances the understanding of generation models in IoT contexts, providing innovative approaches to tackle the challenges of data scarcity and quality while paving the way for more intelligent and adaptable sensing applications."]},{"key":"dc:format","label":"Dc Format","values":["application/pdf"]},{"key":"dc:title","label":"Title","values":["Generation models for internet of things sensing applications"]}]}],"canonical_facts":{"dc:contributor":["Abdelzaher, Tarek","Nahrstedt, Klara","Zhao, Han","Srivastava, Mani"],"dc:creator":["Wang, Tianshi"],"dc:date":["2024-12","2024-12-03"],"dc:description":["Submission original under an indefinite embargo labeled 'Open Access'. The submission was exported from vireo on 2025-03-28 without embargo terms","The student, Tianshi Wang, accepted the attached license on 2024-12-02 at 18:02.","The student, Tianshi Wang, submitted this Dissertation for approval on 2024-12-02 at 18:16.","This Dissertation was approved for publication on 2024-12-03 at 14:59.","DSpace SAF Submission Ingestion Package generated from Vireo submission #21456 on 2025-03-28 at 14:27:49","The widespread deployment of Internet of Things (IoT) sensors has transformed how we observe physical phenomena and integrate computation into everyday life. However, despite the vast amount of data generated by these sensors daily, there remains a critical need for high-quality, task-specific datasets. As deep learning models increasingly demand larger volumes of data, the artificial generation of data for IoT sensing applications has become an essential research area. This dissertation presents a comprehensive exploration of generation models for synthesizing IoT sensing signals. Through seven chapters, it investigates the design, innovation, and practical application of various generative models in addressing key challenges in IoT data generation. Chapter 1 sets the stage by discussing the key challenges motivating the need for generation approaches in IoT sensing and outlines the scope. Chapter 2 demonstrates the effectiveness of discriminative models in generating signals with robust input-output mappings, exemplified by a task that transforms motion sensor signals into human speech audio. Chapter 3 delves into the strength of VAEs in disentangling the factors that influence data generation, highlighting a data augmentation framework for IoT applications as a proof of concept. In Chapter 4, diffusion models are explored, showcasing their capability to generate high-quality data in a vehicle detection scenario. Chapter 5 investigates the condition space of conditional generative models and the potential to manipulate this space for controlled data synthesis. Chapter 6 extends this exploration by proposing methodologies for achieving fine-grained control over the IoT sensing data generation process. Chapter 7 concludes this dissertation. This work advances the understanding of generation models in IoT contexts, providing innovative approaches to tackle the challenges of data scarcity and quality while paving the way for more intelligent and adaptable sensing applications."],"dc:format":["application/pdf"],"dc:identifier":["https://hdl.handle.net/2142/127254"],"dc:language":["en","eng"],"dc:rights":["Copyright 2024 Tianshi Wang"],"dc:subject":["Internet Of Things","Generation Models","Deep Learning"],"dc:title":["Generation models for internet of things sensing applications"],"dc:type":["text","Thesis"],"thesis:degree_discipline":["Computer Science"],"thesis:degree_level":["Dissertation"],"thesis:degree_name":["Ph.D."],"thesis:institution_name":["University of Illinois at Urbana-Champaign"]},"updated_at":"2026-07-22T22:25:03Z"}