Improved tag estimation method for TDMA anticollision protocols using CA-CFAR technique

Abstract

Abstract Radio Frequency Identification (RFID) is considered a key element of the Internet of Things (IoT). It enables objects to be identified and tracked using radio waves. In RFID systems, several tags may be present within the range of the reader, leading to collisions and interference between signals that reduce system performance. Several anti-collision solutions have been proposed to solve this problem based on TDMA (Time-Division Multiple Access) technique, such as the ALOHA, Slotted ALOHA and Dynamic Framed Slotted ALOHA (DFSA) protocols. In this paper, we propose to improve TDMA anti-collision protocols based on the Cell Averaging Constant False Alarm Rate (CA-CFAR) scheme. The CA-CFAR technique is introduced to estimate the number of tags in the physical layer so that the reader can adjust its transmission parameters accordingly and avoid collisions. The obtained results show that this technique achieves good results in terms of system efficiency and the number of required slots to identify all the tags of the system.