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Smokey: Ubiquitous smoking detection with commercial WiFi infrastructures
Ist Teil von
IEEE INFOCOM 2016 - The 35th Annual IEEE International Conference on Computer Communications, 2016, p.1-9
Ort / Verlag
IEEE
Erscheinungsjahr
2016
Link zum Volltext
Quelle
IEEE/IET Electronic Library (IEL)
Beschreibungen/Notizen
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.