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

被引：17

作者：

Ahmad, Uzair ^{[1
]}

Gavrilov, Andrey V. ^{[1
]}

Lee, Young-Koo ^{[1
]}

Lee, Sungyoung ^{[1
]}

机构：

[1] Kyung Hee Univ, Dept Comp Engn, UC Lab, Seoul, South Korea

来源：

SOFT COMPUTING | 2008年 / 12卷 / 07期

关键词：

online incremental learning; indoor location systems; signal strength; self-scalability; context aware classifier;

D O I：

10.1007/s00500-007-0243-2

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

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.

引用

页码：699 / 713

页数：15

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