Accurate and Energy-Efficient GPS-Less Outdoor Localization

作者：Aly Heba; Basalamah Anas; Youssef Moustafa

来源：ACM Transactions on Spatial Algorithms and Systems, 2017, 3(2): 4.

DOI：10.1145/3085575

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Location-based services have become an important part of our daily lives. However, such services require continuous user tracking while preserving the scarce cell-phone battery resource. In this article, we present
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, a system that uses standard cell-phone sensors to provide accurate and energy-efficient outdoor localization.
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is capable of localizing and navigating both pedestrian and in-vehicle users in real time. Our analysis shows that, whether walking or in-vehicle, when the user encounters a road landmark such as going inside a tunnel, ascending a staircase, or even moving over a bump, all these different landmarks affect the inertial sensors on the phone in a unique pattern.
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employs a dead-reckoning localization approach and leverages these road landmarks, among other automatically discovered virtual landmarks, to reset the dead-reckoning accumulated error and achieve accurate localization. To maintain a low energy profile,
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uses only energy-efficient sensors or sensors that are already running for other purposes. Moreover,
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provides a localization confidence measure along with its predicted location. This improves the usability of the predicted location from end users’ perspective.
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We present the design of
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and how it leverages crowd-sourcing to automatically learn virtual landmarks and their locations. Our evaluation results from implementation on different Android devices using different testbeds showing that
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can localize cell-phones in vehicles with a median error of 8.4
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in city roads and 16.6
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on highways and can localize cell-phones carried by pedestrians with a median error of 3.0m. Moreover, compared to the global position system (GPS) and other state-of-the-art systems,
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can extend the battery lifetime by up to 347%, while achieving even better localization results than GPS in the more challenging in-city areas. In addition,
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estimates the localization confidence measure accurately with a median error of 2.3m and 31cm for in-vehicle and pedestrian users, respectively.
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• 出版日期2017