Statistical Learning Theory for Location Fingerprinting in Wireless LANs

Battiti, Roberto and Brunato, Mauro and Villani, Alessandro (2002) Statistical Learning Theory for Location Fingerprinting in Wireless LANs. UNSPECIFIED. (Unpublished)

Download (310Kb) | Preview


    In this paper, techniques and algorithms developed in the framework of statistical learning theory are analyzed and applied to the problem of determining the location of a wireless device by measuring the signal strengths from a set of access points (location fingerprinting). Statistical Learning Theory provides a rich theoretical basis for the development of models starting from a set of examples. Signal strength measurement is part of the normal operating mode of wireless equipment, in particular Wi-Fi, so that no custom hardware is required. The proposed techniques, based on the Support Vector Machine paradigm, have been implemented and compared, on the same data set, with other approaches considered in the literature. Tests performed in a real-world environment show that results are comparable, with the advantage of a low algorithmic complexity in the normal operating phase. Moreover, the algorithm is particularly suitable for classification, where it outperforms the other techniques.

    Item Type: Departmental Technical Report
    Department or Research center: Information Engineering and Computer Science
    Subjects: Q Science > QA Mathematics > QA075 Electronic computers. Computer science
    Uncontrolled Keywords: Context-aware compuing, Location Management, Wi-Fi, Statistical Learning Theory
    Additional Information: Submitted to MobiSys 2003 - May 5-8, 2003 - San Francisco, USA
    Report Number: DIT-02-086
    Repository staff approval on: 21 Jan 2003

    Actions (login required)

    View Item