RFID data cleaning method in heterogeneous space based on linear probabilistic motion state model Online publication date: Fri, 22-Jul-2022
by Shuming Wang
International Journal of Information and Communication Technology (IJICT), Vol. 21, No. 1, 2022
Abstract: In order to overcome the problems of low efficiency, low precision and high load of heterogeneous spatial data cleaning methods, this paper proposes a heterogeneous spatial RFID data cleaning method based on linear probabilistic motion state model. Combining with the infinity of RFID data stream, this method uses sliding window technology to smooth the spatial tag data, and effectively removes the noise in the data. The Bernoulli binomial distribution is used to model the RFID data stream. At the same time, a probabilistic motion model for RFID tags is introduced. The transformation relationship between RFID initial data and tag motion state information is established. The vulnerability data of heterogeneous space is filled by tag motion state information, and heterogeneous spatial data cleaning is realised. The experimental results show that the operation efficiency is always above 95%, the highest data cleaning accuracy is 99.01%, and the lowest operating load is 0.038%.
Existing subscribers:
Go to Inderscience Online Journals to access the Full Text of this article.
If you are not a subscriber and you just want to read the full contents of this article, buy online access here.Complimentary Subscribers, Editors or Members of the Editorial Board of the International Journal of Information and Communication Technology (IJICT):
Login with your Inderscience username and password:
Want to subscribe?
A subscription gives you complete access to all articles in the current issue, as well as to all articles in the previous three years (where applicable). See our Orders page to subscribe.
If you still need assistance, please email subs@inderscience.com
Our Blog 
Follow us on Twitter 
Visit us on Facebook