Mobile and Ubiquitous Systems: Computing, Networking, and Services. 10th International Conference, MOBIQUITOUS 2013, Tokyo, Japan, December 2-4, 2013, Revised Selected Papers

Research Article

MVPTrack: Energy-Efficient Places and Motion States Tracking

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  • @INPROCEEDINGS{10.1007/978-3-319-11569-6_60,
        author={Chunhui Zhang and Ke Huang and Guanling Chen and Linzhang Wang},
        title={MVPTrack: Energy-Efficient Places and Motion States Tracking},
        proceedings={Mobile and Ubiquitous Systems: Computing, Networking, and Services. 10th International Conference, MOBIQUITOUS 2013, Tokyo, Japan, December 2-4, 2013,  Revised Selected Papers},
        proceedings_a={MOBIQUITOUS},
        year={2014},
        month={12},
        keywords={Place sensing Energy efficiency Place awareness},
        doi={10.1007/978-3-319-11569-6_60}
    }
    
  • Chunhui Zhang
    Ke Huang
    Guanling Chen
    Linzhang Wang
    Year: 2014
    MVPTrack: Energy-Efficient Places and Motion States Tracking
    MOBIQUITOUS
    Springer
    DOI: 10.1007/978-3-319-11569-6_60
Chunhui Zhang1,*, Ke Huang1,*, Guanling Chen1,*, Linzhang Wang2,*
  • 1: University of Massachusetts Lowell
  • 2: Nanjing University
*Contact email: czhang@cs.uml.edu, khuang@cs.uml.edu, glchen@cs.uml.edu, lzwang@nju.edu.cn

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.