Resource management in sensing services with audio applications

Le, Long Nguyen Thang

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https://hdl.handle.net/2142/97414 Copy

Description

Title

Resource management in sensing services with audio applications

Author(s)

Le, Long Nguyen Thang

Issue Date

2017-04-20

Director of Research (if dissertation) or Advisor (if thesis)

Jones, Douglas L.

Doctoral Committee Chair(s)

Nahrstedt, Klara

Committee Member(s)

• Srikant, Rayadurgam
• Veeravalli, Venugopal V.

Department of Study

Electrical & Computer Eng

Discipline

Electrical & Computer Engr

Degree Granting Institution

University of Illinois at Urbana-Champaign

Degree Name

Ph.D.

Degree Level

Dissertation

Keyword(s)

• Resource management
• Sensing services
• Internet of Things
• Audio classification
• Guided-processing
• Feature-sharing

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).

Graduation Semester

2017-05

Type of Resource

text

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http://hdl.handle.net/2142/97414

Copyright and License Information

Copyright 2017 Long Le

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