Deliberation for intuition: a framework for energy-efficient trip detection on cellular phones

  • Authors:

  • Yifei Jiang;Du Li;Guang Yang;Qin Lv;Zhigang Liu

  • Affiliations:

  • University of Colorado, Boulder, Boulder, CO, USA;Nokia Research Center, Palo Alto , Palo Alto , CA, USA;Nokia Research Center, Palo Alto , Palo Alto , CA, USA;University of Colorado, Boulder, Boulder, CO, USA;Nokia Research Center, Palo Alto , Palo Alto, CA, USA

  • Venue:
  • Proceedings of the 13th international conference on Ubiquitous computing
  • Year:
  • 2011

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Abstract

Trip detection is a fundamental issue in many context-sensitive information services on mobile devices. It aims to automatically recognize significant places and trips between them. The key challenge is how to minimize energy consumption while maintaining high accuracy. Previous works that use GPS/WiFi sampling are accurate but energy efficiency is low and does not improve over time. Learning from the human decision making process, we propose an energy-efficient trip detection framework that consists of two modes: The deliberation mode learns cell-id patterns using GPS/WiFi based localization methods; the intuition mode only uses cell-ids and learned patterns for trip detection; transition between the two modes is controlled by parameters that are also learned. We evaluated our framework using real-life traces of six people over five months. Our experiments demonstrate that its energy consumption decreases rapidly as users' activities manifest regularity over time.