Maximum a posteriori (MAP)-based tag estimation method for dynamic framed-slotted ALOHA (DFSA) in RFID systems

Abstract

Radio frequency identification (RFID) is a non-contact technology that uses radio frequency electromagnetic fields to transfer data from a tag attached to an object, for the purposes of automatic identification and tracking. One of the common problems that arise in any RFID deployment is the collision between tags which reduces the efficiency of the RFID system. Dynamic framed-slotted ALOHA (DFSA) is one of the most popular approaches to resolve the tag collision problem. In DFSA, each tag randomly selects one of the time slots of a frame and transmits its data at the slot. Unless the tag successfully transmits its data to a reader, it will try again in the next frame. It is widely known that the optimal performance of framed-slotted ALOHA is achieved when the frame size (i.e., number of time slots) is equal to the number of tags to be identified. So, a reader dynamically adjusts the next frame size according to the number of tags. Thus, it is important to accurately estimate the number of tags. In this article, we propose an accurate maximum a posteriori (MAP)-based tag estimation method with low computational complexity. The idea behind our method is to more accurately determine the most potential number of tags which draws the observed results on the basis of both a posteriori probability and a priori probability. Simulation results show that our method improves the accuracy of tag estimation and the speed of tag identification.