Crowdsensing Based Barometer Sensor Calibration Using Smartphones

Since first appeared in the Google Galaxy Nexus in 2011, the barometer sensor now becomes a very common sensor on smartphones. Although the barometer is originally used to improve the efficiency of GPS positioning, developers and users have gradually discovered that it is very useful in many other applications, such as localization/navigation, sport/activity and weather/travel. Unfortunately, the current smartphone barometer measurement is not accurate, and efficiently calibrating a large number of smartphone barometers is rather challenging. In this paper, we proposed CBSC, an effective and accurate approach to calibrate a large number of barometer sensors automatically. CBSC makes use of low-power barometer on smartphone and does not require reference points or human assistant. We concentrate on the user encounter detection based on sensor data correlation, peer to peer calibration based on encounter event, and calibrate all barometers by solving a minimum dominating set problem. The field studies show that CBSC can get an accuracy of within 0.1 hpa in 82% cases. Compared to the traditional works, CBSC is more practical and the accuracy is enough for most application scenarios.