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A Multisensor Architecture Providing Location-based Services for Smartphones

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Abstract

This paper introduces a multisensor architecture to fuse data acquired from different sensors available in commodity smartphones in order to build accurate location-based services, and pursuing a good balance between accuracy and performance. Using scale invariant features from the images captured using the smartphone camera, we perform a matching process against previously obtained images to determine the current location of the device. Several refinements are introduced to improve the performance and the scalability of our proposal. Location fingerprinting, based on IEEE 802.11, will be used to determine a cluster of physical points, or zone, where the device seems to be according to the received signal strength. In this way, we will reduce the number of images to analyze to those contained in the tentative zone. Additionally, accelerometers will also be considered in order to improve the system performance, by means of a motion estimator. This set of techniques enables a wide range of location-based applications.

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Notes

  1. http://cs.unc.edu/~ccwu/siftgpu

  2. A set of cells previously established to get a heterogeneous set of observations.

  3. The intensive image processing involved by the described algorithm leaded us to use a GPU based implementation, as described in Section 3. Though several less computationally intensive alternative feature extractor and descriptor techniques have been described in the literature (see for example [20], and the references therein), they tend to be still too hard to be computed in the smartphones in terms of both processing time and battery consumption.

  4. See also footnote 3 in relation to this possibility.

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Acknowledgement

This work was supported by the Spanish MICINN, Plan E funds, under Grant TIN2009-14475-C04-02/01.

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Correspondence to Antonio J. Ruiz-Ruiz.

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Ruiz-Ruiz, A.J., Canovas, O. & Lopez-de-Teruel, P.E. A Multisensor Architecture Providing Location-based Services for Smartphones. Mobile Netw Appl 18, 310–325 (2013). https://doi.org/10.1007/s11036-012-0423-x

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