计算机科学技术学报 ›› 2019,Vol. 34 ›› Issue (5): 1007-1019.doi: 10.1007/s11390-019-1957-1

所属专题： Software Systems

• Special Section on Software Systems 2019 • 上一篇    下一篇

CBSC:基于群智感知的手机气压传感器自动校准系统

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
  • 收稿日期:2019-02-27 修回日期:2019-07-07 出版日期:2019-08-31 发布日期:2019-08-31
  • 作者简介: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.
  • 基金资助:
    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.

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.

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基于移动群智感知的软件系统通过大量普通用户的集体智慧可以完成大规模的复杂任务。本文建立了一个典型的群智感知系统，可以高效的对大量的智能手机传感器进行校准。手机气压传感器目前已经十分普遍，并在很多应用中被广泛使用，比如定位、环境感知和行为识别等。可惜的是目前的智能手机气压传感器是不够精确的，要高效的校准大量的手机气压传感器具有较大的挑战。本文通过提出一个基于群智感知的智能手机气压传感器校准系统来应对这一问题。该系统仅利用低功耗的气压传感器而不依赖于设置其他参考点和人为的手动操作。本文提出了基于隐式马尔科夫模型的一对一校准模型，然后通过提出解决一个最小支配集的方法实现所有传感器的校准。实验表明CBSC在84%的情况下误差低于0.1hpa。与传统的校准方法相比，CBSC更实用，精度也满足要求。该软件系统的经验也对今后开发其它移动群智感知软件系统提供了很高的参考价值。

关键词: 群智感知系统, 智能手机感知, 气压传感器校准

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 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.

Key words: crowdsensing system, smartphone sensing, barometer calibration

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