IEEE_Tr_CNS.pdf

Recent renewed interest in radio frequency identification (RFID) suggests that low-cost passive RFID tags will play a vital role in the Internet of Things (IoT), bridging the gap between the physical and virtual realms. In RFID systems based on dynamic frame-slotted ALOHA (DFSA), it is essential to precisely estimate the number of tags for proper operation of the systems. The challenge with tag quantity estimation is the limited amount of information available for estimation. In particular, a tag estimation is typically performed on a per-frame basis using a single set of statistics collected during a frame. Moreover, the tag population in a reader’s interrogation zone is steadily increasing with the advent of the IoT era. Nevertheless, most estimators perform well even for highly dense tag populations. In this study, we investigate how the tag population affects the performance of tag estimators. In particular, by establishing several strong laws of large numbers (SLLN) results, we show that the estimates always converge to the actual values as the number of tags becomes large. Based on the SLLN results, we propose a novel estimation method where estimates are given as fixed points of contraction mappings. Finally, we examine the key properties of the proposed estimator.