TITLE

Mining the Internet of Things: Detection of False-Positive RFID Tag Reads using Low-Level Reader Data

AUTHOR(S)
Keller, Thorben
PUB. DATE
May 2011
SOURCE
University of St. Gallen, Business Dissertations;5/13/2011, p1
SOURCE TYPE
Dissertation
DOC. TYPE
Article
ABSTRACT
The notion of the "Internet of Things" (IoT) describes the vision of ubiquitous sensor technologies that seamlessly link arbitrary physical objects to their digital counterparts in the network. In recent years, it was particularly the rapid diffusion of Radio Frequency Identification (RFID) in supply chain and distribution center processes that has contributed to the emergence and popularity of the IoT concept. However, technological constraints currently delay fully reliable and productive use of the technology. One major constraint is the problem of false-positive RFID tag reads, i.e., when an RFID transponder is read unintentionally by an RFID reader. This problem is studied in the context of RFID enabled outgoing goods processing at a distribution center in Unna, Germany. The RFID installation in this METRO Group center was part of the largest operational rollout of the technology in the European retail sector. In the examined scenario it is necessary to distinguish between tracked RFID tagged pallets that are loaded onto trucks and other pallets that are also in range of the reader. If all detected pallets are reported to the warehouse management system as being shipped, the resulting incorrect invoices mean stores have to pay for goods that they neither ordered nor received. It is evident, therefore, that a solution to this problem is a must before a reliable and productive use of RFID technology in distribution center processes is feasible. Currently, the few conceptual approaches presented in the literature that deal with this problem suffer from fundamental weaknesses. This thesis addresses those weaknesses by means of a machine learning based approach that makes use of the low-level reader data collected when scanning for transponders. For this purpose, meaningful attributes have been identified that help describe characteristics specific to pallets that have been loaded and to pallets that cause false-positives. This approach was able to minimize the number of possible incorrect shipments to only 1 per 4,500 pallet loadings -- a solution that clearly exceeded METRO Group's expectations.
ACCESSION #
82754301

 

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