Novel RFID method for faster convergence of tag estimation on dynamic frame size ALOHA algorithms

Abstract

This paper introduces a novel RFID method for accelerating the tag estimation performance in dynamic framed slotted ALOHA (DFSA) algorithms. Assuming a critical scenario where the number of tags in the interrogation zone is much greater than the initial frame size, the proposed Double-Approximation (DA) method uses, besides the backlog estimation to adjust the size of first frames, a second approximation that successively reduces the tags available to transmit until achieving a faster convergence to the first frame sizes. Once the convergence threshold is achieved, a more accurate tag estimation can be calculated and all temporarily disabled tags become able to transmit again. Simulation results show that all DFSA algorithms implemented with the DA method outperforms the traditional DFSA in terms of throughput and reduction of simultaneous tag signals.