Probabilistic counting algorithms for data base applications
Journal of Computer and System Sciences
Gossip-Based Computation of Aggregate Information
FOCS '03 Proceedings of the 44th Annual IEEE Symposium on Foundations of Computer Science
Approximate Aggregation Techniques for Sensor Databases
ICDE '04 Proceedings of the 20th International Conference on Data Engineering
Synopsis diffusion for robust aggregation in sensor networks
SenSys '04 Proceedings of the 2nd international conference on Embedded networked sensor systems
TAG: a Tiny AGgregation service for Ad-Hoc sensor networks
OSDI '02 Proceedings of the 5th symposium on Operating systems design and implementationCopyright restrictions prevent ACM from being able to make the PDFs for this conference available for downloading
Robust approximate aggregation in sensor data management systems
ACM Transactions on Database Systems (TODS)
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Aggregation counting is any procedure designed to solve the following problem: a number n of agents produces a fixed length binary message, and a central station produces an estimate of n from the bit-by-bit OR of the messages, which is therefore duplicate-insensitive. Such procedures are applicable to a situation where each of n independent sensors broadcasts the message to be used to estimate the count. A mathematically brilliant solution to this problem, due to Flajolet and Martin (1985) [1], is unfortunately affected by substantial bias and error. In this note we outline an alternative approach, which uses the Flajolet-Martin technique as a preparatory step and substantially reduces both error and bias. Specifically, the standard deviation of the count estimate drops from ~110% to ~20% of the estimated value.