MVPTrack: Energy-Efficient Places and Motion States Tracking

  • Chunhui Zhang
  • Ke Huang
  • Guanling Chen
  • Linzhang Wang
Conference paper
Part of the Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering book series (LNICST, volume 131)

Abstract

Contextual information such as a person’s meaningful places (Different from a person’s location (raw coordinates), place is an indoor or outdoor area where a person usually conducts some activity, in other words where it is meaningful to the person, such as home, office rooms, restaurants etc.) could provide intelligence to many smartphone apps. However, acquiring this context attribute is not straightforward and could easily drain the battery. In this paper, we propose M(Move)V(Vehicle)P(Place)Track, a continuous place and motion state tracking framework with a focus on improving the energy efficiency of place entrance detection through two techniques: (1) utilizing the mobility change not only for finding the sleeping opportunities for the high energy sensors, but also for providing hint for place entrance detection, (2) leveraging the place history for fast place entrance detection. We evaluated MVPTrack based on traces collected by five persons over two weeks. The evaluation results showed that MVPTrack used 58 % less energy than previous work and provided a much faster place entrance detection approach.

Keywords

Place sensing Energy efficiency Place awareness 

References

  1. 1.
    Zhang, C., Ding, X., Chen, G., Huang, K., Ma, X., Yan, B.: Nihao: a predictive smartphone application launcher. In: ser. MobiCASE’12 (2012)Google Scholar
  2. 2.
    Kim, D.H., Kim, Y., Estrin, D., Srivastava, M.B.: Sensloc: sensing everyday places and paths using less energy. In: ser. SenSys’10 (2010)Google Scholar

Copyright information

© Institute for Computer Sciences, Social Informatics and Telecommunications Engineering 2014

Authors and Affiliations

  • Chunhui Zhang
    • 1
  • Ke Huang
    • 1
  • Guanling Chen
    • 1
  • Linzhang Wang
    • 2
  1. 1.Computer Science DepartmentUniversity of Massachusetts LowellLowellUSA
  2. 2.Nanjing UniversityNanjingChina

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