Browsing by Author "Lee, Yueh-Feng"

Now showing 1 - 2 of 2

Detecting movement of beacons in location-tracking wireless sensor networks
(2007)
Kuo, Sheng-Po
;
Kuo, Hsiao-Ju
;
Tseng, Ya-Chee
;
Lee, Yueh-Feng
;
資訊工程學系
Localization is a critical issue in wireless sensor networks. In most localization schemes, there are beacons being placed as references to determine the positions of objects or events appearing in the sensing field. The underlying assumption is that beacons are always static. In this work, we define a new Beacon Movement Detection (BMD) problem. Assuming that there are unnoticed changes of locations of some beacons in the system, this problem is concerned about how to automatically monitor such situations and identify these beacons. Removal of such beacons in the positioning engine may improve the localization accuracy. Two schemes are proposed to solve the BMD problem. Finally, we evaluate how these solutions can improve the accuracy of localization schemes in case that there are unnoticed movement of some beacons. Simulation results show that our solutions alleviate 53% the decrease of positioning accuracy caused by the exceptional beacon movement.
Indoor Localization: Automatically Constructing Today's Radio Map by iRobot and RFIDs
(2009)
Yeh, Lun-Wu
;
Hsu, Ming-Shiou
;
Lee, Yueh-Feng
;
Tseng, Yu-Chee
;
資訊工程學系
For outdoor localization, GPS already provides a satisfactory solution. For indoor localization, however, a globally usable solution is still missing. One promising direction that is proposed recently is the fingerprinting-based solution. It involves a training phase to collect the radio signal strength (RSS) patterns in fields where localization is needed into a database (called radio map). The radio signal could be from WiFi access points, GSM base stations, or other RF-based networks. Then, during the positioning phase, an object which is interested in its own location can collect its current RSS pattern and compare it against the radio map established in the training phase to identify its possible location. We present an interesting system based a robot and numerous cheap RFID tags deployed on the ground to automate the training process and, more importantly, to frequently update radio maps to reflect the current RSS patterns. This not only significantly reduces human labors but also improves positioning accuracy.