Trajectory Event Cleaning for Mobile RFID Objects

With the rapid development of Radio Frequency Identification (RFID), sensor and wireless technologies, a large amount of trajectory data of moving objects are emerging, and trajectory data mining has received more and more attentions recently. However, since the data collected by sensors and RFID re...

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Bibliographic Details
Main Authors: Guoqiong Liao, Yu, Philip S., Qianhui Zhong, Sihong Xie, Zhen Shen, Changxuan Wan, Dexi Liu
Format: Conference Proceeding
Language:English
Subjects:
Cleaning
data cleaning
Image edge detection
Interpolation
Mobile communication
mobile objects
Probabilistic logic
Radiofrequency identification
RFID
Trajectory
trajectory events
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Summary:With the rapid development of Radio Frequency Identification (RFID), sensor and wireless technologies, a large amount of trajectory data of moving objects are emerging, and trajectory data mining has received more and more attentions recently. However, since the data collected by sensors and RFID readers are usually noisy, it is necessary and meaningful to clean up the noise, including missing detection events and cross detection events, so as to provide high quality data for various applications using trajectory data. Cleaning up the trajectory events should take into account of uncertainty of location and unreliability of event detection at the same time. In the paper, we first discuss the rules to distinguish between normal detection events and false detection events in the trajectories, using constraints on continuous motion between adjacent detection regions and direct moving time between neighboring physical regions. Then, as a unified cleaning framework, we establish a probabilistic region connection graph to represent region detection features, region connection relationships, and region transition probabilities of neighboring physical regions. Focusing on interpolating missing events, we suggest two path-based probabilistic interpolating strategies, namely, the Most Likely Path (MLP) strategy and the Highest Weighting Probability Path (HWPP) strategy. Also, we discuss pruning rules of candidate paths for reducing computational cost. Finally, we conduct experiments over simulation data to demonstrate the effectiveness and efficiency of the proposed methods.
ISSN:1551-6245
2375-0324
DOI:10.1109/MDM.2014.23