Towards Constant-Time Cardinality Estimation for Large-Scale RFID Systems

Cardinality estimation is the process to survey the quantity of tags in a RFID system. Generally, the cardinality is estimated by exchanging information between reader(s) and tags. To ensure the time efficiency and accuracy of estimation, numerous probability-based approaches have been proposed, most of which follow a similar way of minimizing the number of required time slots from tags to reader. The overall execution time of the estimator, however, is not necessarily minimized. The estimation accuracy of those approaches also largely depends on the repeated rounds, leading to a dilemma of choosing efficiency or accuracy. In this paper, we propose BFCE, a Bloom Filter based Cardinality Estimator, which only needs a constant number of time slots to meet desired estimation accuracy, regardless of the actual tag cardinality. The overall communication overhead is also significantly cut down, as the reader only needs to broadcast a constant number of messages for parameter setting. Results from extensive simulations under various tag IDs distributions shows that BFCE is accurate and highly efficient. In terms of the overall execution time, BFCE is 30 times faster than ZOE and 2 times faster than SRC in average, the two state-of-the-arts estimation approaches.