New sampling-based summary statistics for improving approximate query answers
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ACM Transactions on Computer Systems (TOCS)
TAG: a Tiny AGgregation service for ad-hoc sensor networks
ACM SIGOPS Operating Systems Review - OSDI '02: Proceedings of the 5th symposium on Operating systems design and implementation
Gossip-Based Computation of Aggregate Information
FOCS '03 Proceedings of the 44th Annual IEEE Symposium on Foundations of Computer Science
A scalable distributed information management system
Proceedings of the 2004 conference on Applications, technologies, architectures, and protocols for computer communications
Synopsis diffusion for robust aggregation in sensor networks
SenSys '04 Proceedings of the 2nd international conference on Embedded networked sensor systems
Correctness of a gossip based membership protocol
Proceedings of the twenty-fourth annual ACM symposium on Principles of distributed computing
ACM SIGOPS Operating Systems Review
Proceedings of the Fifth Workshop on Programming Languages and Operating Systems
Distributed data classification in sensor networks
Proceedings of the 29th ACM SIGACT-SIGOPS symposium on Principles of distributed computing
Identifying frequent items in a network using gossip
Journal of Parallel and Distributed Computing
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OTM'10 Proceedings of the 2010 international conference on On the move to meaningful internet systems
Spectra: robust estimation of distribution functions in networks
DAIS'12 Proceedings of the 12th IFIP WG 6.1 international conference on Distributed Applications and Interoperable Systems
The state of peer-to-peer network simulators
ACM Computing Surveys (CSUR)
The VLDB Journal — The International Journal on Very Large Data Bases
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We propose a novel gossip-based technique that allows each node in a system to estimate the distribution of values held by other nodes. We observe that the presence of duplicate values does not significantly affect the distribution of values in samples collected through gossip, and based on that explore different data synopsis techniques that optimize space and time while allowing nodes to accumulate information. Unlike previous aggregation schemes, our approach focuses on allowing all nodes in the system to compute an estimate of the entire distribution in a decentralized and efficient manner. We evaluate our approach through simulation, showing that it is simple and scalable, and that it allows all nodes in the system to converge to a satisfactory estimate of the distribution in a small number of rounds.