Indoor positioning methods based on fingerprinting and radio signals rely on the quality of the radio map. For example, for room-level classification purposes, it is required that the signal observations related to each room exhibit significant differences in their RSSI values. However, it is difficult to verify and visualize that separability since radio maps are constituted by multi-dimensional observations whose dimension is directly related to the number of access points or monitors being employed for localization purposes. In this paper, we propose a refinement cycle for passive indoor positioning systems, which is based on dimensionality reduction techniques, to evaluate the quality of a radio map. By means of these techniques and our own data representation, we have defined two different visualization methods to obtain graphical information about the quality of a particular radio map in terms of overlapping areas and outliers. That information will be useful to determine whether new monitors are required or some existing ones should be moved. We have performed an exhaustive experimental analysis based on a variety of different scenarios, some deployed by our own research group and others corresponding to a well-known existing dataset widely analyzed by the community, in order to validate our proposal. As we will show, among the different combinations of data representation methods and dimensionality reduction techniques that we discuss, we have found that there are some specific configurations that are more useful in order to perform the refinement process.
We present a binary classifier named WiFiBoost which makes use of RSSIs from the existing 802.11 access points (AP) in order to infer whether a device is inside a building. Our weak learner is based on the feature of RSSI comparison between pairs of APs. The resulting model is really compact and efficient, and it can be stored in the mobile device. Our classifier also minimizes the negative effect of unpredicted AP failures or changes in the network infrastructure, which will result in a moderate impact on the classification error under those circumstances.
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