The Sheaf Laplacian: A Topological Framework for Data Fusion and Consensus in Distributed Sensing Networks

Abstract

We argue here that traditional network models, which are overwhelmingly based on the mathematical construct of a simple graph, are fundamentally insufficient for capturing the complexity of modern distributed systems. Such systems are characterized by heterogeneous agents with diverse capabilities, high-dimensional and multi-modal data streams, and intricate, context-dependent relationships that cannot be adequately described by a simple connection or a scalar weight. The limitations of these classical models necessitate a new mathematical language, one with far greater expressive power. We have found that sheaf theory provides us with such a language. Moreover, we show that the sheaf Laplacian is a suitable mechanism for data fusion and establishing consensus within distributed sensing networks.