Reinforcement learning based anti-collision algorithm for RFID systems

Abstract

Efficient collision arbitration protocol facilitates fast tag identification in radio frequency identification (RFID) systems. EPCGlobal-Class1- Generation2 (EPC-C1G2) protocol is the current standard for collision arbitration in commercial RFID systems. However, the main drawback of this protocol is that it requires excessive message exchanges between tags and the reader for its operation. This wastes energy of the already resource-constrained RFID readers. Hence, in this work, reinforcement learning based anti-collision protocol (RL-DFSA) is proposed to address the energy efficient collision arbitration problem in the RFID system. The proposed algorithm continuously learns and adapts to the changes in the environment by devising an optimal policy. The proposed RL-DFSA was evaluated through extensive simulations and compared with the variants of EPC-C1G2 algorithms that are currently being used in the commercial readers. Based on the results, it is concluded that RLDFSA performs equal or better than EPC-C1G2 protocol in delay, throughput and time system efficiency when simulated for sparse and dense environments while requiring one order of magnitude lesser control message exchanges between the reader and the tags.