Recovery properties of distributed cluster head election using reaction–diffusion

Abstract

Chemical reaction–diffusion is a basic component of morphogenesis, and can be used to obtain interesting and unconventional self-organizing algorithms for swarms of autonomous agents, using natural or artificial chemistries. However, the performance of these algorithms in the face of disruptions has not been sufficiently studied. In this paper we evaluate the use of reaction–diffusion for the morphogenetic engineering of distributed coordination algorithms, in particular, cluster head election in a distributed computer system. We consider variants of reaction–diffusion systems around the activator–inhibitor model, able to produce spot patterns. We evaluate the robustness of these models against the deletion of activator peaks that signal the location of cluster heads, and the destruction of large patches of chemicals. Three models are selected for evaluation: the Gierer–Meinhardt Activator–Inhibitor model, the Activator–Substrate Depleted model, and the Gray–Scott model. Our results reveal a trade-off between these models. The Gierer–Meinhardt model is more stable, with rare failures, but is slower to recover from disruptions. The Gray–Scott model is able to recover more quickly, at the expense of more frequent failures. The Activator–Substrate model lies somewhere in the middle.