Location-aware computing: a neural network model for determining location in wireless LANs

Battiti, Roberto and Le, Nhat Thang and Villani, Alessandro (2002) Location-aware computing: a neural network model for determining location in wireless LANs. UNSPECIFIED. (Unpublished)

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    The strengths of the RF signals arriving from more access points in a wireless LANs are related to the position of the mobile terminal and can be used to derive the location of the user. In a heterogeneous environment, e.g. inside a building or in a variegated urban geometry, the received power is a very complex function of the distance, the geometry, the materials. The complexity of the inverse problem (to derive the position from the signals) and the lack of complete information, motivate to consider flexible models based on a network of functions (neural networks). Specifying the value of the free parameters of the model requires a supervised learning strategy that starts from a set of labeled examples to construct a model that will then generalize in an appropriate manner when confronted with new data, not present in the training set. The advantage of the method is that it does not require ad-hoc infrastructure in addition to the wireless LAN, while the flexible modeling and learning capabilities of neural networks achieve lower errors in determining the position, are amenable to incremental improvements, and do not require the detailed knowledge of the access point locations and of the building characteristics. A user needs only a map of the working space and a small number of identified locations to train a system, as evidenced by the experimental results presented.

    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: Location- and context-aware computing, wireless LANs, IEEE802.11b, neural networks, machine learning.
    Report Number: DIT-02-083
    Repository staff approval on: 21 Jan 2003

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