Efficient dual bias Q-Algorithm and optimum weights for EPC Class 1 Generation 2 Protocol

Abstract

Passive RFID tags, which have no self-battery and just backscatter the energy from a reader, share a common channel. It causes a tag-to-tag collision problem when at least two or more tags communicate to the reader simultaneously. The dynamic frame slotted Aloha (DFS-Aloha) protocol is one of the well-known anti-collision algorithms to solve this problem. To maximize the system performance and enhance the reading speed of Aloha protocols, we propose a frame-size estimator which is a modified version of Q-Algorithm. To maximize the system performance, the optimum bias values of the proposed estimator are searched by using Least Square method from mathematical model of DFS-Aloha. Simulation results show that the proposed estimation algorithm enhances the system performance. The proposed estimation algorithm takes less identification time than DFS-Aloha with the existing Q-Algorithm based on EPC Class 1 Generation 2 protocol.