In this brief we propose a distributed δ-consensus protocol in directed networks of dynamic agents having communication delays. We provide convergence analysis for such consensus algorithm under stochastic switching communication graphs, and then present some generic criteria for solving the average consensus problem. We also show that directed delayed networks of dynamic agents can achieve ave...

A variety of predictive models is currently used to map the spatial distribution of earth surface processes and landforms. In this study, we tested statistical consensus methods in order to improve the predictive accuracy of geomorphological models. The distributions of 12 geomorphological formations were recorded at a resolution of 25ha in a sub-arctic landscape in northern Finland. Nine envir...

This paper revisits the problem of multi-agent consensus from a graph signal processing perspective. By defining the graph filter from the consensus protocol, we establish the direct relation between average consensus of multi-agent systems and filtering of graph signals. This relation not only provides new insights of the average consensus, it also turns out to be a powerful tool to design eff...

We study the convergence speed of distributed iterative algorithms for the consensus and averaging problems, with emphasis on the latter. We first consider the case of a fixed communication topology. We show that a simple adaptation of a consensus algorithm leads to an averaging algorithm. We prove lower bounds on the worst-case convergence time for various classes of linear, time-invariant, di...

We first propose an unbiased consensus algorithm in wireless networks via random broadcast, by which all the nodes tend to the initial average in mean almost surely. The innovation of the algorithm lies in that it can work in any connected topology, in spite of the possible collisions from simultaneous data arriving at receivers in a shared channel. Based on the consensus algorithm, we propose ...

Matrix representation with parsimony (MRP) can be used to combine trees in the supertree or the consensus settings. However, despite its popularity, it is still unclear whether MRP is really a consensus method or whether it behaves more like the total evidence approach. Previous simulations have shown that it approximates total evidence trees, whereas other studies have depicted similarities wi...