The stunning popularity of mobile devices in recent years has spurred interest in querying and tracking their location for a wide variety of applications. Many naive approaches to localizing mobile devices, however, suffer from excessive energy use, poor accuracy in indoor environments, or excessive complexity presented to the application developer. We ask: does processing and evaluating location queries using a cloud server help reduce complexity and inaccuracy for these queries without overusing the limited energy available on mobile devices? Also, can this approach be combined with a variety of individual localization methods and dynamically-adjusted localization frequency to further reduce energy use? To answer these questions, we create a simple prototype using Android smartphones and evaluate its performance against common naive approaches and Google's geofences location query system. Our results show energy-use reductions of up to 75% and dramatically improved precision of 45-60% compared to less sophisticated approaches, which compare favorably to Google's geofences while allowing more fine-grained selection of regions of interest.
Copyright of the original work is retained by the author.
The export option will allow you to export the current search results of the entered query to a file. Different
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