Threshold-Based Network Structural Dynamics

The interest in dynamic processes on networks is steadily rising in recent years. In this paper, we consider the $(\alpha,\beta)$-Thresholded Network Dynamics ($(\alpha,\beta)$-Dynamics), where $\alpha\leq \beta$, in which only structural dynamics (dynamics of the network) are allowed, guided by local thresholding rules executed in each node. In particular, in each discrete round $t$, each pair of nodes $u$ and $v$ that are allowed to communicate by the scheduler, computes a value $\mathcal{E}(u,v)$ (the potential of the pair) as a function of the local structure of the network at round $t$ around the two nodes. If $\mathcal{E}(u,v) < \alpha$ then the link (if it exists) between $u$ and $v$ is removed; if $\alpha \leq \mathcal{E}(u,v) < \beta$ then an existing link among $u$ and $v$ is maintained; if $\beta \leq \mathcal{E}(u,v)$ then a link between $u$ and $v$ is established if not already present. The microscopic structure of $(\alpha,\beta)$-Dynamics appears to be simple, so that we are able to rigorously argue about it, but still flexible, so that we are able to design meaningful microscopic local rules that give rise to interesting macroscopic behaviors. Our goals are the following: a) to investigate the properties of the $(\alpha,\beta)$-Thresholded Network Dynamics and b) to show that $(\alpha,\beta)$-Dynamics is expressive enough to solve complex problems on networks. Our contribution in these directions is twofold. We rigorously exhibit the claim about the expressiveness of $(\alpha,\beta)$-Dynamics, both by designing a simple protocol that provably computes the $k$-core of the network as well as by showing that $(\alpha,\beta)$-Dynamics is in fact Turing-Complete. Second and most important, we construct general tools for proving stabilization that work for a subclass of $(\alpha,\beta)$-Dynamics and prove speed of convergence in a restricted setting.Comment: 29 pages, extension of the Post-print containing all proofs, to appear in SIROCCO 202

Self-Reconfigurable Robots Topodynamic

Abstract — Modules connected to each other form a network. So, a modular robot is a module network. In the case of reconfigurable robots, the topology of this network evolves. We propose to ground the study of self-reconfigurable robots in a framework inspired by graph theory and cellular automata. We separate topological aspects from metrical ones, by defining the notion of graph topodynamic, and we provide a distributed algorithm which transforms a quadruped robot into a chain. I