Mobile Computing, Applications, and Services. First International ICST Conference, MobiCASE 2009, San Diego, CA, USA, October 26-29, 2009, Revised Selected Papers

Research Article

Energy-Efficient Localization via Personal Mobility Profiling

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  • @INPROCEEDINGS{10.1007/978-3-642-12607-9_14,
        author={Ionut Constandache and Shravan Gaonkar and Matt Sayler and Romit Choudhury and Landon Cox},
        title={Energy-Efficient Localization via Personal Mobility Profiling},
        proceedings={Mobile Computing, Applications, and Services. First International ICST Conference, MobiCASE 2009, San Diego, CA, USA, October 26-29, 2009, Revised Selected Papers},
        proceedings_a={MOBICASE},
        year={2012},
        month={10},
        keywords={},
        doi={10.1007/978-3-642-12607-9_14}
    }
    
  • Ionut Constandache
    Shravan Gaonkar
    Matt Sayler
    Romit Choudhury
    Landon Cox
    Year: 2012
    Energy-Efficient Localization via Personal Mobility Profiling
    MOBICASE
    Springer
    DOI: 10.1007/978-3-642-12607-9_14
Ionut Constandache1,*, Shravan Gaonkar2,*, Matt Sayler1,*, Romit Choudhury1,*, Landon Cox1,*
  • 1: Duke University
  • 2: University of Illinois at Urbana Champaign
*Contact email: ionut@cs.duke.edu, gaonkar@ieee.org, sayler@cs.duke.edu, lpcox@cs.duke.edu, romit@ee.duke.edu

Abstract

Location based services are on the rise, many of which assume GPS based localization. Unfortunately, GPS incurs an unacceptable energy cost that can reduce the phone’s battery life to less than ten hours. Alternate localization technology, based on WiFi or GSM, improve battery life at the expense of localization accuracy. This paper quantifies this important tradeoff that underlies a wide range of emerging applications. To address this tradeoff, we show that humans can be profiled based on their mobility patterns, and such profiles can be effective for location prediction. Prediction reduces the energy consumption due to continuous localization. Driven by measurements from Nokia N95 phones, we develop an energy-efficient localization framework called . Evaluation on real user traces demonstrates the possibility of achieving good localization accuracy for a realistic energy budget.