Validating Passive Localization Methods for Occupancy Sensing Systems in Wireless Environments: A Case Study

López-de-Teruel, Pedro E.; García, Félix; Cánovas, Óscar

doi:10.1016/j.procs.2016.08.012

Cited by 4 publications

(1 citation statement)

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“…Our recent work [ 2 ] is more focused on the latter type of positioning systems, since they do not require the explicit collaboration of the users nor the installation of special purpose applications on their mobile devices. Generally speaking, users are reluctant to install apps that are battery consuming, and additionally, there are serious limitations to obtain RSSI (Received Signal Strength Indicator) information from some operating systems.…”

Section: Introductionmentioning

confidence: 99%

Using Dimensionality Reduction Techniques for Refining Passive Indoor Positioning Systems Based on Radio Fingerprinting

López-de-Teruel

Cánovas

García

2017

Sensors

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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.

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Section: Introductionmentioning

confidence: 99%