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Jones, Douglas L. Nahrstedt, Klara Srikant, Rayadurgam Veeravalli, Venugopal V. Le, Long Nguyen Thang 2017-08-10T19:15:36Z 2017-08-10T19:15:36Z 2017-04-20 2017-05 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'. 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). No. of bitstreams: 3 LE-DISSERTATION-2017.pdf: 4054097 bytes, checksum: 7288b28df4a08b26ff57bcd397d1dc55 (MD5) LICENSE.txt: 4204 bytes, checksum: c23b1af983b23632cc42176cc8b0f07a (MD5) PROQUEST_LICENSE.txt: 4550 bytes, checksum: 65320fa26ba136a5fefe83998aff1090 (MD5) Previous issue date: 2017-04-20 application/pdf http://hdl.handle.net/2142/97414 en Copyright 2017 Long Le Resource management Sensing services Internet of Things Audio classification Guided-processing Feature-sharing Resource management in sensing services with audio applications text text Electrical & Computer Eng Electrical & Computer Engr University of Illinois at Urbana-Champaign Dissertation Ph.D.