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CBSC: A Crowdsensing System for Automatic Calibrating of Barometers

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Abstract

The mobile crowdsensing software systems can complete large-scale and complex sensing tasks with the help of the collective intelligence from large numbers of ordinary users. In this paper, we build a typical crowdsensing system, which can efficiently calibrate large numbers of smartphone barometer sensors. The barometer sensor now becomes a very common sensor on smartphones. It is very useful in many applications, such as positioning, environment sensing and activity detection. Unfortunately, most smartphone barometers today are not accurate enough, and it is rather challenging to efficiently calibrate a large number of smartphone barometers. Here, we try to achieve this goal by designing a crowdsensingbased smartphone calibration system, which is called CBSC. It makes use of low-power barometers on smartphones and needs few reference points and little human assistant. We propose a hidden Markov model for peer-to-peer calibration, and calibrate all the barometers by solving a minimum dominating set problem. The field studies show that C BSC can get an accuracy of within 0.1 hPa in 84% cases. Compared with the traditional solutions, CBSC is more practical and the accuracy is satisfying. The experience gained when building this system can also help the development of other crowdsensing-based systems.

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Authors and Affiliations

  1. College of Computer Science and Technology, Nanjing University of Aeronautics and Astronautics, Nanjing, 211100, China

    Hai-Bo Ye & Li Sheng

  2. School of Computer Science and Engineering, Nanjing University of Science and Technology, Nanjing, 210094, China

    Xuan-Song Li

  3. School of Computer Science and Engineering, Southeast University, Nanjing, 211189, China

    Kai Dong

Authors
  1. Hai-Bo Ye
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  2. Xuan-Song Li
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  3. Li Sheng
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  4. Kai Dong
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Correspondence to Hai-Bo Ye.

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Ye, HB., Li, XS., Sheng, L. et al. CBSC: A Crowdsensing System for Automatic Calibrating of Barometers. J. Comput. Sci. Technol. 34, 1007–1019 (2019). https://doi.org/10.1007/s11390-019-1957-1

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  • DOI: https://doi.org/10.1007/s11390-019-1957-1

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