SensLoc: sensing everyday places and paths using less energy

  • Authors:

  • Donnie H. Kim;Younghun Kim;Deborah Estrin;Mani B. Srivastava

  • Affiliations:

  • UCLA CSD CENS, UCLA EED NESL;UCLA CSD CENS, UCLA EED NESL;UCLA CSD CENS, UCLA EED NESL;UCLA CSD CENS, UCLA EED NESL

  • Venue:
  • Proceedings of the 8th ACM Conference on Embedded Networked Sensor Systems
  • Year:
  • 2010

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Abstract

Continuously understanding a user's location context in colloquial terms and the paths that connect the locations unlocks many opportunities for emerging applications. While extensive research effort has been made on efficiently tracking a user's raw coordinates, few attempts have been made to efficiently provide everyday contextual information about these locations as places and paths. We introduce SensLoc, a practical location service to provide such contextual information, abstracting location as place visits and path travels from sensor signals. SensLoc comprises of a robust place detection algorithm, a sensitive movement detector, and an on-demand path tracker. Based on a user's mobility, SensLoc proactively controls active cycle of a GPS receiver, a WiFi scanner, and an accelerometer. Pilot studies show that SensLoc can correctly detect 94% of the place visits, track 95% of the total travel distance, and still only consume 13% of energy than algorithms that periodically collect coordinates to provide the same information.