Fast and Reliable Detection and Identification of Missing RFID Tags in the Wild

Abstract

Radio-frequency identification (RFID) systems have been deployed to detect and identify missing products by affixing them with cheap passive RFID tags and monitoring them with RFID readers. Existing missing tag detection and identification protocols require the tag population to contain only those tags whose IDs are already known to the reader. However, in reality, tag populations often contain tags with unknown IDs, called unexpected tags. These unexpected tags cause unexpected false positives, i.e. , due to them, missing tags are detected as present. We take the first step toward addressing the problem of detecting and identifying missing tags from a population that contains unexpected tags. Our protocol, RUN, uses standardized frame slotted Aloha for communication between tags and readers. It executes multiple frames with different seeds to reduce the effects of unexpected false positives. At the same time, it minimizes the missing tag detection and identification time by first estimating the number of unexpected tags in the population and then using it along with the false-positive probability to obtain optimal frame sizes and minimum number of times Aloha frames should be executed to mitigate the effects of false positives. RUN works with multiple readers with overlapping regions. It is easy to deploy, because it is implemented on readers as a software module and does not require any modifications to tags or to the communication protocol between the tags and the readers. We implemented RUN along with four major missing tag detection and identification protocols, namely, TRP, IIP, MTI, and SFMTI, and the fastest tag ID collection protocol TH and compared them side by side. Our performance evaluation results show that RUN is the only protocol that achieves required reliability in the presence of unexpected tags, whereas the best existing protocol achieves a maximum reliability of only 67%. RUN identifies 100% of missing tags in the presence of unexpected tags, whereas the best existing protocol identifies a maximum of only 60% of missing tags.