RFID Category Stocktaking With Unexpected Tags and Varying Missing Rate

One of the prominent applications of RFID technology is stocktaking. In many scenarios, the users concern about the stocktaking of categories of objects rather than their individual IDs. Existing approaches that utilize the inventory list for rapid identification can also be used for the stocktaking of categories. However, the number of missing categories of objects varies in different applications and affects the efficiency of stocktaking. Moreover, it is not uncommon that some unexpected tags that are not in the inventory list to appear in the target area and cause mistaken stocktaking. In this study, we investigate the category stocktaking problem with unexpected tags and varying category missing rate. We exploit the compressed Bloom filter to deactivate unexpected tags, dynamically adapt to different category missing rate by applying different protocols, and adjust the parameters of these protocols. We perform extensive simulations to validate the performance of our proposed approach. The results show that our approach significantly outperforms the state-of-the-art solutions.