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An efficient decentralized clustering algorithm for aggregation of noisy multi-mean data

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Abstract

We describe VarClust, a gossip-based decentralized clustering algorithm designed to support multi-mean decentralized aggregation in energy-constrained wireless sensor networks. We empirically demonstrate that VarClust is at least as accurate as, and requires less node-to-node communication (and hence consumes less energy) than, a state-of-the-art aggregation approach, affinity propagation. This superiority holds for both the clustering and aggregation phases of inference, and is demonstrated over a range of noise levels and for a range of random and small-world graph topologies.

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Notes

  1. We consider this to be a worst-case scenario, in which the physical properties of the environment being sensed can play no role in the inference of a sensible communication topology.

  2. We exclude the pathological case in which system noise is greater than the variance of \(\varGamma \).

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Buadhacháin, S.Ó., Provan, G. An efficient decentralized clustering algorithm for aggregation of noisy multi-mean data. J Heuristics 21, 301–328 (2015). https://doi.org/10.1007/s10732-014-9259-9

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