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Journal of Computer Science and Technology ›› 2019, Vol. 34 ›› Issue (5): 1007-1019.

Special Issue: Software Systems

• Special Section on Software Systems 2019 •

### CBSC: A Crowdsensing System for Automatic Calibrating of Barometers

Hai-Bo Ye1, Member, CCF, Xuan-Song Li2, Member, CCF, Li Sheng1, Kai Dong3

1. 1 College of Computer Science and Technology, Nanjing University of Aeronautics and Astronautics, Nanjing 211100, China;
2 School of Computer Science and Engineering, Nanjing University of Science and Technology, Nanjing 210094, China;
3 School of Computer Science and Engineering, Southeast University, Nanjing 211189, China
• Received:2019-02-27 Revised:2019-07-07 Online:2019-08-31 Published:2019-08-31
• About author:Hai-Bo Ye received his Ph.D. degree in computer science from Nanjing University, Nanjing, in 2016. He is currently an associate professor in the College of Computer Science and Technology at Nanjing University of Aeronautics and Astronautics, Nanjing. His research interests include mobile localization and navigation, mobile and pervasive computing.
• Supported by:
The work was supported by the National Natural Science Foundation of China under Grant Nos. 61702261 and 61702263, the China Postdoctoral Science Foundation under Grant No. 2017M621742, and the Foundation of State Key Laboratory for Novel Software Technology of China under Grant No. KFKT2017B15.

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 CBSC 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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