MAC: Missing Tag Iceberg Queries for Multi-Category RFID Systems

Recently radio frequency identification (RFID) is promoting the rapid development of Internet of Things and has been widely applied in numerous industrial applications such as inventory management, object tracking and smart logistics, etc. Tags in some RFID applications can be classified into multiple categories according to the types of objects they are attached to. One of the important functionalities for the multi-category RFID systems is the missing tag cardinality estimation of each category. This paper focuses on solving the missing tag iceberg query problem for multi-category RFID systems, i.e., to determine a set of categories, whose missing tags are more than a threshold, with a required reliability. We firstly propose two basic missing tag iceberg query schemes called MAC-SZE and MAC-HZE, which adopt the singleton-zero estimator and homogeneous-zero estimator, respectively. To further improve the query efficiency, we then propose two segmented enhanced schemes called MAC-SSZE and MAC-SHZE, which eliminate the unnecessary slots in each frame. We conduct theoretical analysis to guarantee the required reliability is satisfied. The simulations are finally conducted and the results illustrate that the proposed missing tag iceberg query schemes greatly outperform other existing one.