Space/time trade-offs in hash coding with allowable errors
Communications of the ACM
Batch conflict resolution algorithm with progressively accurate multiplicity estimation
Proceedings of the 2004 joint workshop on Foundations of mobile computing
AINA '05 Proceedings of the 19th International Conference on Advanced Information Networking and Applications - Volume 2
Adaptive splitting protocols for RFID tag collision arbitration
Proceedings of the 7th ACM international symposium on Mobile ad hoc networking and computing
Fast and reliable estimation schemes in RFID systems
Proceedings of the 12th annual international conference on Mobile computing and networking
Finding popular categories for RFID tags
Proceedings of the 9th ACM international symposium on Mobile ad hoc networking and computing
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Given a large set of RFID tags, we are interested in determining the categories of tags that are present in the shortest time possible. Since there can be more than one tag present in a particular category, pure randomized strategies that rely on resolving individual tags are very inefficient. Instead, we rely on a pseudo-random strategy that utilizes a uniform hash function to accurately identify all t categories present among a given set of ψ tags with high probability. We propose two algorithms: (a) a single frame algorithm that determines the optimal frame size, and (b) a probabilistic version where the frame size is fixed, and we select the probability to minimize the number of frames needed for identification. Both of these algorithms run in time linear to the number of categories present, t. We show that our approach significantly outperforms existing algorithms for category identification. The performance of our algorithms is within a constant factor of the lower bound.