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 EnLoc. Evaluation on real user traces demonstrates the possibility of achieving good localization accuracy for a realistic energy budget.
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© 2010 ICST Institute for Computer Science, Social Informatics and Telecommunications Engineering
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Constandache, I., Gaonkar, S., Sayler, M., Choudhury, R.R., Cox, L. (2010). Energy-Efficient Localization via Personal Mobility Profiling. In: Phan, T., Montanari, R., Zerfos, P. (eds) Mobile Computing, Applications, and Services. MobiCASE 2009. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol 35. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-12607-9_14
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DOI: https://doi.org/10.1007/978-3-642-12607-9_14
Publisher Name: Springer, Berlin, Heidelberg
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