Bayesian Transmission Strategy for Framed ALOHA Based RFID Protocols

Transmission control strategies can increase the throughput of the shared wireless channel and thus accelerate the identification of large RFID tag populations. In this paper, we present a Bayesian strategy that minimizes the response time to changes in the number of RFID tags transmitting by updating the tag number estimate after each slot in an ALOHA frame. If the current frame size is no longer considered to be optimal, our control strategy aborts the current frame and triggers the start of a new frame size. The transmission control strategy is evaluated with the help of a scalable RFID simulation engine that implements the ISO 18000-6 C protocol and that supports different pathloss, fading, capture, and tag mobility models. Our evaluation shows that the Bayesian transmission strategy has a higher throughput than other approaches that only update the estimate at the end of the frame. The evaluation also shows that our Bayesian approach outperforms the Q algorithm specified in ISO 18000-6 Part C at the expense of a significant amount of computations.