Publication Type

Conference Proceeding Article

Version

publishedVersion

Publication Date

4-2016

Abstract

Even though indoor smoking ban is being put into practice in civilized countries, existing vision or sensor-based smoking detection methods cannot provide ubiquitous smoking detection. In this paper, we take the first attempt to build a ubiquitous passive smoking detection system, which leverages the patterns smoking leaves on WiFi signals to identify the smoking activity even in the non-line-of-sight and through-wall environments. We study the behaviors of smokers and leverage the common features to recognize the series of motions during smoking, avoiding the target-dependent training set to achieve the high accuracy. We design a foreground detection based motion acquisition method to extract the meaningful information from multiple noisy subcarriers even influenced by posture changes. Without requirements of target’s compliance, we leverage the rhythmical patterns of smoking to reduce the detection false positives. We prototype Smokey with the commodity WiFi infrastructure and evaluate its performance in real environments. Experimental results show Smokey is accurate and robust in various scenarios.

Discipline

Digital Communications and Networking | Software Engineering

Research Areas

Software and Cyber-Physical Systems

Publication

Proceeding of the 35th IEEE Annual International Conference on Computer Communications, San Francisco, 2016 April 10-14

First Page

1

Last Page

9

ISBN

9781467399531

Identifier

10.1109/INFOCOM.2016.7524399

Publisher

IEEE

City or Country

San Francisco, CA, USA

Additional URL

https://doi.org/10.1109/INFOCOM.2016.7524399

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