Context-aware, self-scaling Fuzzy ArtMap for received signal strength based location systems

Authors:
Uzair Ahmad;Andrey V. Gavrilov;Young-Koo Lee;Sungyoung Lee
Affiliations:
Kyung Hee University, UC Lab, Department of Computer Engineering, Seoul, South Korea;Kyung Hee University, UC Lab, Department of Computer Engineering, Seoul, South Korea;Kyung Hee University, UC Lab, Department of Computer Engineering, Seoul, South Korea;Kyung Hee University, UC Lab, Department of Computer Engineering, Seoul, South Korea
Venue:
Soft Computing - A Fusion of Foundations, Methodologies and Applications - Special issue on neural networks for pattern recognition and data mining
Year:
2008

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Abstract

Location awareness is the key capability of mobile computing applications. Despite high demand, indoor location technologies have not become truly ubiquitous mainly due to their requirements of costly infrastructure and dedicated hardware components. Received signal strength (RSS) based location systems are poised to realize economical ubiquity as well as sufficient accuracy for variety of applications. Nevertheless high resolution RSS based location awareness requires tedious sensor data collection and training of classifier which lengthens location system development life cycle. We present a rapid development approach based on online and incremental learning method which significantly reduces development time while providing competitive accuracy in comparison with other methods. ConSelFAM (Context-aware, Self-scaling Fuzzy ArtMap) extends the Fuzzy ArtMap neural network system. It enables on the fly expansion and reconstruction of location systems which is not possible in previous systems.