Self-learning Indoor Locating Algorithm for Wireless Sensor Networks
Date Issued
2009
Date
2009
Author(s)
Lin, Jia-Shian
Abstract
Tracing the location of a specific item has induced abundant applications with the development of wireless sensor networks. In past years, different location algorithms have been proposed for indoor or outdoor environments. Some of these use additional equipments, such as GPS[21][22][23], Infrared[24] and Ultrasound[25]. However, these equipments will increase the power consumption and system cost. In this paper, we propose a Self-learning Indoor Locating Algorithm for Wireless Sensor Networks and use only the embedded RF chip to improve the PM (Pattern Matching) algorithm. We made some improvements on saving training time, computation, and higher the positioning precision. Experiments show that our proposed algorithm not only lower training time but also higher positioning precision in 41%.
Subjects
Wireless sensor networks (WSN)
Indoor Locating Algorithm
Pattern Matching
Fingerprinting
Type
thesis
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