Sheaf theory and the sheaf Laplacian are proposed as a topological framework for data fusion and consensus in distributed sensing networks.
Sheaves are the canonical datastructure for sensor integration
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abstract
A sensor integration framework should be sufficiently general to accurately represent all information sources, and also be able to summarize information in a faithful way that emphasizes important, actionable information. Few approaches adequately address these two discordant requirements. The purpose of this expository paper is to explain why sheaves are the canonical data structure for sensor integration and how the mathematics of sheaves satisfies our two requirements. We outline some of the powerful inferential tools that are not available to other representational frameworks.
fields
cs.DC 1years
2026 1verdicts
UNVERDICTED 1representative citing papers
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The Sheaf Laplacian: A Topological Framework for Data Fusion and Consensus in Distributed Sensing Networks
Sheaf theory and the sheaf Laplacian are proposed as a topological framework for data fusion and consensus in distributed sensing networks.