{"id":{"repo_id":"uiuc","oai_identifier":"oai:www.ideals.illinois.edu:2142/127289"},"canonical_url":"https://search.dev.ndltd.org/etd/uiuc/oai:www.ideals.illinois.edu:2142/127289","repository":{"repo_id":"uiuc","name":"University of Illinois - Urbana-Champaign","base_url":"https://www.ideals.illinois.edu/oai-pmh"},"display":{"title":"Towards micro foundation models for robust multimodal IoT sensing","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":["Kimura, Tomoyoshi"],"institution":"University of Illinois at Urbana-Champaign","degree_name":"M.S.","degree_level":"Thesis","degree_discipline":"Computer Science","degree_department":null,"school":null,"contributors":["Abdelzaher, Tarek"],"advisors":[],"committee_chairs":[],"committee_members":[],"year":2024,"date_issued":"2024-12-10","date_published":"2024-12-10","updated_at":"2026-07-22T22:25:03Z","subjects":["Foundation Models","Internet Of Things","Self-supervised Learning"],"languages":["en","eng"],"rights":["Copyright 2024 Tomoyoshi Kimura"],"rights_urls":[],"identifier_entries":[]},"links":{"outbound_url":"https://hdl.handle.net/2142/127289","outbound_label":"Handle","outbound_source":"dc:identifier"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor","label":"Contributor","values":["Abdelzaher, Tarek"]},{"key":"dc:creator","label":"Author","values":["Kimura, Tomoyoshi"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date","label":"Dc Date","values":["2024-12-10","2024-12"]},{"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":["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":["Foundation Models","Internet Of Things","Self-supervised 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 Tomoyoshi Kimura"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier","label":"Identifier","values":["https://hdl.handle.net/2142/127289"]}]},{"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, Tomoyoshi Kimura, accepted the attached license on 2024-12-09 at 21:11.","The student, Tomoyoshi Kimura, submitted this Thesis for approval on 2024-12-09 at 21:18.","This Thesis was approved for publication on 2024-12-10 at 16:56.","DSpace SAF Submission Ingestion Package generated from Vireo submission #21559 on 2025-03-28 at 14:28:37","The thesis argues and advocates for the feasibility and utility of micro foundation models (µFMs), a key direction for future smart IoT/CPS systems that exploits advances in self-supervised pre-training to support robust intelligent inference tasks. We demonstrate key beneficial properties such as latent representation independence from the downstream task, robustness to domain shifts, ability to learn from unlabeled data, and enhanced structural resiliency of edge systems. Importantly, we demonstrate the emergence of these properties after pre-training with only moderate amounts of unlabeled data, earning the name µFMs. To make the argument, evaluate model efficacy, and surface some of the underlying challenges, this thesis describes a vibration-based µFM, called VibroFM, pre-trained with moderate amounts of unlabeled acoustic and seismic sensing data, to support target classification and tracking applications. VibroFM is pre-trained in an environment-agnostic fashion using unlabeled sensor data. It can then be fine-tuned to a given deployment using a small amount of in-situ labeled data. The paper shows that VibroFM (i) improves the robustness of several downstream tasks, (ii) efficiently adapts to different environmental conditions (using only small amounts of fine-tuning), (iii) allows few-shot generalization to unseen targets, and (iv) generalizable to multiple downstream tasks each at a minimal labeling and system cost. We further show that VibroFM can execute in real-time on embedded sensor nodes. We compare the robustness and performance of VibroFM to conventional supervised deep neural networks, showing the advantages of the former. Combined with the feasibility of executing µFMs in resource-limited settings and the sufficiency of only moderate amounts of data for their pre-training, we conclude the importance of micro foundation models as a promising research direction for the IoT/CPS community."]},{"key":"dc:format","label":"Dc Format","values":["application/pdf"]},{"key":"dc:title","label":"Title","values":["Towards micro foundation models for robust multimodal IoT sensing"]}]}],"canonical_facts":{"dc:contributor":["Abdelzaher, Tarek"],"dc:creator":["Kimura, Tomoyoshi"],"dc:date":["2024-12-10","2024-12"],"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, Tomoyoshi Kimura, accepted the attached license on 2024-12-09 at 21:11.","The student, Tomoyoshi Kimura, submitted this Thesis for approval on 2024-12-09 at 21:18.","This Thesis was approved for publication on 2024-12-10 at 16:56.","DSpace SAF Submission Ingestion Package generated from Vireo submission #21559 on 2025-03-28 at 14:28:37","The thesis argues and advocates for the feasibility and utility of micro foundation models (µFMs), a key direction for future smart IoT/CPS systems that exploits advances in self-supervised pre-training to support robust intelligent inference tasks. We demonstrate key beneficial properties such as latent representation independence from the downstream task, robustness to domain shifts, ability to learn from unlabeled data, and enhanced structural resiliency of edge systems. Importantly, we demonstrate the emergence of these properties after pre-training with only moderate amounts of unlabeled data, earning the name µFMs. To make the argument, evaluate model efficacy, and surface some of the underlying challenges, this thesis describes a vibration-based µFM, called VibroFM, pre-trained with moderate amounts of unlabeled acoustic and seismic sensing data, to support target classification and tracking applications. VibroFM is pre-trained in an environment-agnostic fashion using unlabeled sensor data. It can then be fine-tuned to a given deployment using a small amount of in-situ labeled data. The paper shows that VibroFM (i) improves the robustness of several downstream tasks, (ii) efficiently adapts to different environmental conditions (using only small amounts of fine-tuning), (iii) allows few-shot generalization to unseen targets, and (iv) generalizable to multiple downstream tasks each at a minimal labeling and system cost. We further show that VibroFM can execute in real-time on embedded sensor nodes. We compare the robustness and performance of VibroFM to conventional supervised deep neural networks, showing the advantages of the former. Combined with the feasibility of executing µFMs in resource-limited settings and the sufficiency of only moderate amounts of data for their pre-training, we conclude the importance of micro foundation models as a promising research direction for the IoT/CPS community."],"dc:format":["application/pdf"],"dc:identifier":["https://hdl.handle.net/2142/127289"],"dc:language":["en","eng"],"dc:rights":["Copyright 2024 Tomoyoshi Kimura"],"dc:subject":["Foundation Models","Internet Of Things","Self-supervised Learning"],"dc:title":["Towards micro foundation models for robust multimodal IoT sensing"],"dc:type":["text","Thesis"],"thesis:degree_discipline":["Computer Science"],"thesis:degree_level":["Thesis"],"thesis:degree_name":["M.S."],"thesis:institution_name":["University of Illinois at Urbana-Champaign"]},"updated_at":"2026-07-22T22:25:03Z"}