{"id":{"repo_id":"uiuc","oai_identifier":"oai:www.ideals.illinois.edu:2142/97414"},"canonical_url":"https://search.dev.ndltd.org/etd/uiuc/oai:www.ideals.illinois.edu:2142/97414","repository":{"repo_id":"uiuc","name":"University of Illinois - Urbana-Champaign","base_url":"https://www.ideals.illinois.edu/oai-pmh"},"display":{"title":"Resource management in sensing services with audio applications","abstract":"Middleware abstractions, or services, that can bridge the gap between the increasingly pervasive sensors and the sophisticated inference applications exist, but they lack the necessary resource-awareness to support high data-rate sensing modalities such as audio/video. This work therefore investigates the resource management problem in sensing services, with application in audio sensing. First, a modular, data-centric architecture is proposed as the framework within which optimal resource management is studied. Next, the guided-processing principle is proposed to achieve optimized trade-off between resource (energy) and (inference) performance. On cascade-based systems, empirical results show that the proposed approach significantly improves the detection performance (up to 1.7x and 4x reduction in false-alarm and miss rate, respectively) for the same energy consumption, when compared to the duty-cycling approach. Furthermore, the guided-processing approach is also generalizable to graph-based systems. Resource-efficiency in the multiple-application setting is achieved through the feature-sharing principle. Once applied, the method results in a system that can achieve 9x resource saving and 1.43x improvement in detection performance in an example application. Based on the encouraging results above, a prototype audio sensing service is built for demonstration. An interference-robust audio classification technique with limited training data would prove valuable within the service, so a novel algorithm with the desired properties is proposed. The technique combines AI-gram time-frequency representation and multidimensional dynamic time warping, and it outperforms the state-of-the-art using the prominent-region-based approach across a wide range of (synthetic, both stationary and transient) interference types and signal-to-interference ratios, and also on field recordings (with areas under the receiver operating characteristic and precision-recall curves being 91% and 87%, respectively).","abstract_html":"Middleware abstractions, or services, that can bridge the gap between the increasingly pervasive sensors and the sophisticated inference applications exist, but they lack the necessary resource-awareness to support high data-rate sensing modalities such as audio/video. This work therefore investigates the resource management problem in sensing services, with application in audio sensing. First, a modular, data-centric architecture is proposed as the framework within which optimal resource management is studied. Next, the guided-processing principle is proposed to achieve optimized trade-off between resource (energy) and (inference) performance. On cascade-based systems, empirical results show that the proposed approach significantly improves the detection performance (up to 1.7x and 4x reduction in false-alarm and miss rate, respectively) for the same energy consumption, when compared to the duty-cycling approach. Furthermore, the guided-processing approach is also generalizable to graph-based systems. Resource-efficiency in the multiple-application setting is achieved through the feature-sharing principle. Once applied, the method results in a system that can achieve 9x resource saving and 1.43x improvement in detection performance in an example application. Based on the encouraging results above, a prototype audio sensing service is built for demonstration. An interference-robust audio classification technique with limited training data would prove valuable within the service, so a novel algorithm with the desired properties is proposed. The technique combines AI-gram time-frequency representation and multidimensional dynamic time warping, and it outperforms the state-of-the-art using the prominent-region-based approach across a wide range of (synthetic, both stationary and transient) interference types and signal-to-interference ratios, and also on field recordings (with areas under the receiver operating characteristic and precision-recall curves being 91% and 87%, respectively).","abstract_has_math":false,"creators":["Le, Long Nguyen Thang"],"institution":"University of Illinois at Urbana-Champaign","degree_name":"Ph.D.","degree_level":"Dissertation","degree_discipline":"Electrical & Computer Engr","degree_department":null,"school":null,"contributors":["Jones, Douglas L.","Nahrstedt, Klara","Srikant, Rayadurgam","Veeravalli, Venugopal V."],"advisors":[],"committee_chairs":[],"committee_members":[],"year":2017,"date_issued":"2017-08-10T19:15:36Z","date_published":"2017-08-10T19:15:36Z","updated_at":"2026-07-22T22:24:34Z","subjects":["Resource management","Sensing services","Internet of Things","Audio classification","Guided-processing","Feature-sharing"],"languages":["en"],"rights":["Copyright 2017 Long Le"],"rights_urls":[],"identifier_entries":[]},"links":{"outbound_url":"http://hdl.handle.net/2142/97414","outbound_label":"Handle","outbound_source":"dc:identifier"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor","label":"Contributor","values":["Jones, Douglas L.","Nahrstedt, Klara","Srikant, Rayadurgam","Veeravalli, Venugopal V."]},{"key":"dc:creator","label":"Author","values":["Le, Long Nguyen Thang"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date","label":"Dc Date","values":["2017-08-10T19:15:36Z","2017-04-20","2017-05"]},{"key":"dc:type","label":"Dc Type","values":["text"]},{"key":"thesis:degree_discipline","label":"Discipline","values":["Electrical & Computer 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":["Resource management","Sensing services","Internet of Things","Audio classification","Guided-processing","Feature-sharing"]}]},{"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 Long Le"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier","label":"Identifier","values":["http://hdl.handle.net/2142/97414"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description","label":"Description","values":["Middleware abstractions, or services, that can bridge the gap between the increasingly pervasive sensors and the sophisticated inference applications exist, but they lack the necessary resource-awareness to support high data-rate sensing modalities such as audio/video. This work therefore investigates the resource management problem in sensing services, with application in audio sensing. First, a modular, data-centric architecture is proposed as the framework within which optimal resource management is studied. Next, the guided-processing principle is proposed to achieve optimized trade-off between resource (energy) and (inference) performance. On cascade-based systems, empirical results show that the proposed approach significantly improves the detection performance (up to 1.7x and 4x reduction in false-alarm and miss rate, respectively) for the same energy consumption, when compared to the duty-cycling approach. Furthermore, the guided-processing approach is also generalizable to graph-based systems. Resource-efficiency in the multiple-application setting is achieved through the feature-sharing principle. Once applied, the method results in a system that can achieve 9x resource saving and 1.43x improvement in detection performance in an example application. Based on the encouraging results above, a prototype audio sensing service is built for demonstration. An interference-robust audio classification technique with limited training data would prove valuable within the service, so a novel algorithm with the desired properties is proposed. The technique combines AI-gram time-frequency representation and multidimensional dynamic time warping, and it outperforms the state-of-the-art using the prominent-region-based approach across a wide range of (synthetic, both stationary and transient) interference types and signal-to-interference ratios, and also on field recordings (with areas under the receiver operating characteristic and precision-recall curves being 91% and 87%, respectively).","Submission original under an indefinite embargo labeled 'Open Access'. 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This work therefore investigates the resource management problem in sensing services, with application in audio sensing. First, a modular, data-centric architecture is proposed as the framework within which optimal resource management is studied. Next, the guided-processing principle is proposed to achieve optimized trade-off between resource (energy) and (inference) performance. On cascade-based systems, empirical results show that the proposed approach significantly improves the detection performance (up to 1.7x and 4x reduction in false-alarm and miss rate, respectively) for the same energy consumption, when compared to the duty-cycling approach. Furthermore, the guided-processing approach is also generalizable to graph-based systems. Resource-efficiency in the multiple-application setting is achieved through the feature-sharing principle. Once applied, the method results in a system that can achieve 9x resource saving and 1.43x improvement in detection performance in an example application. Based on the encouraging results above, a prototype audio sensing service is built for demonstration. An interference-robust audio classification technique with limited training data would prove valuable within the service, so a novel algorithm with the desired properties is proposed. The technique combines AI-gram time-frequency representation and multidimensional dynamic time warping, and it outperforms the state-of-the-art using the prominent-region-based approach across a wide range of (synthetic, both stationary and transient) interference types and signal-to-interference ratios, and also on field recordings (with areas under the receiver operating characteristic and precision-recall curves being 91% and 87%, respectively).","Submission original under an indefinite embargo labeled 'Open Access'. The submission was exported from vireo on 2017-08-10 without embargo terms","The student, Long Le, accepted the attached license on 2017-04-19 at 18:03.","The student, Long Le, submitted this Dissertation for approval on 2017-04-19 at 19:49.","This Dissertation was approved for publication on 2017-04-20 at 16:13.","DSpace SAF Submission Ingestion Package generated from Vireo submission #10915 on 2017-08-10 at 13:43:22","Made available in DSpace on 2017-08-10T19:15:36Z (GMT). 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