Abstract
Decentralized coordination of multi-agent communication networks underpins autonomous UAV swarms, sensor meshes, and cyber-physical systems that must operate without a single point of failure, yet quantitative design rules linking topology degradation to coordination collapse are still lacking. This paper aims to characterize the fault tolerance of a discrete linear consensus protocol that drives mobile agents’ scalar states toward a common value over a time-varying communication graph. The convergence rate and fault tolerance budget of the protocol are explicitly tied to the algebraic connectivity λ2 of the graph Laplacian, and three failure regimes—random independent failures (M1), sequential staged failures (M2), and targeted attacks on the highest-degree hubs (M3)—are analyzed using a full-factorial Monte-Carlo experiment of 1620 runs implemented in Python with the Mesa framework and NetworkX. Critical failure thresholds φc are 0.42–0.51 for M1, 0.38–0.48 for M2, and 0.28–0.38 for M3; a Kruskal–Wallis test (p < 0.001) confirms the ordering φcM3<φcM2<φcM1, providing quantitative design rules for fault-tolerant consensus-driven networks.