Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/163301
Title: Ubiquitous acoustic sensing on commodity IoT devices: a survey
Authors: Cai, Chao
Zheng, Rong
Luo, Jun
Keywords: Engineering::Computer science and engineering
Issue Date: 2022
Source: Cai, C., Zheng, R. & Luo, J. (2022). Ubiquitous acoustic sensing on commodity IoT devices: a survey. IEEE Communications Surveys and Tutorials, 24(1), 432-454. https://dx.doi.org/10.1109/COMST.2022.3145856
Journal: IEEE Communications Surveys and Tutorials
Abstract: With the proliferation of Internet-of-Things (IoT) devices, acoustic sensing attracts significant attention in recent years. It exploits acoustic transceivers such as microphones and speakers beyond their primary functions, namely recording and playing, to enable novel applications and new user experiences. In this paper, we present the first systematic survey on recent advances in ubiquitous acoustic sensing using commodity IoT hardware with a frequency range below 24 kHz. We propose a general framework that categorizes main building blocks of acoustic sensing systems. This framework encompasses three layers, i.e., device, core technique, and application. The device layer includes basic hardware components, acoustic platforms, as well as the air-borne and structure-borne channel characteristics. The core technique layer encompasses key mechanisms to generate acoustic signals (waveforms) and to extract useful temporal, spatial, and spectral information from received signals. The application layer builds upon the functions offered by the core techniques to realize different acoustic sensing applications. We highlight unique challenges due to the limitations of physical devices and acoustic channels and how they are mitigated or overcame by core processing techniques and application-specific solutions. Finally, research opportunities and future directions are discussed to spawn further in-depth investigation on IoT-enabled ubiquitous acoustic sensing.
URI: https://hdl.handle.net/10356/163301
ISSN: 1553-877X
DOI: 10.1109/COMST.2022.3145856
Schools: School of Computer Science and Engineering 
Rights: © 2022 IEEE. All rights reserved.
Fulltext Permission: none
Fulltext Availability: No Fulltext
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