A Case-Based Approach for Indoor Location



Location is an important dimension for context-awareness
in ubiquitous devices. Nowadays different techniques are used alone or
together to determine the position of a person or object. One aspect of
the problem concerns to indoor location. Various authors propose the
analysis of Radio Frequency (RF) footprints.
In this paper we defend that case-based reasoning can make an important
contribution for location from RF footprints. We apply an empirical dissimilarity
metric for footprint retrieval and compare this approach with
the results obtained with a neural network and C5.0 learning algorithms.
The RF footprints are obtained from a Global System for Mobile Communications
and General Packet Radio Service (GSM/GPRS) network.
Signals from these networks are particularly complex when compared to
the ones obtained from WiFi or Bluetooth networks.


Ubiquitous Computing

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