Abstract
There are two kinds of abnormal conditions in the RFID-enabled supply chain such as forgery and miss operation, but the existing anti-counterfeiting model is not used to distinguish them. In this regard, based on the definition of abnormal activity and its dependencies, this paper proposes the abnormality detection rules of the “frequent pattern†yielded by big data techniques. From a multi-dimensional perspective, combined with EPC data and prior information sent from the previous nodes, it can effectively distinguish between forgery activities and operational anomalies. Consequently, asecure visualization system for anti-counterfeiting and anomaly monitoring in rfid-enabled supply chain is implemented with satisfactory results
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callMore From: DEStech Transactions on Environment, Energy and Earth Sciences
Disclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
