A decentralised diagnosis method with probabilistic cellular automata - Inria - Institut national de recherche en sciences et technologies du numérique Access content directly
Conference Papers Year : 2023

A decentralised diagnosis method with probabilistic cellular automata

Abstract

The decentralised diagnosis problem consists in the detection of a certain amount of defects in a distributed network. Here, we tackle this problem in the context of two-dimensional cellular automata with three states : neutral, alert and defect. When the density of defects is below a given threshold, we want the alert state to coexist with the neutral state while when this density is above the threshold, we want the alert state to invade the whole grid. We present two probabilistic rules to answer this problem. The rst one is isotropic and is studied with numerical simulations. The second one is dened on Toom's neighbourhood and is examined with an analytical point of view. These solutions constitute a rst step towards a broader study of the decentralised diagnosis problem on more general networks.
quorumD-FatesMarchandMarcovici-hal-2023-v1b.pdf (397.79 Ko) Télécharger le fichier
Licence : CC BY NC ND - Attribution - NonCommercial - NoDerivatives

Dates and versions

hal-04094230 , version 1 (12-05-2023)

Licence

Attribution

Identifiers

Cite

Nazim Fatès, Régine Marchand, Irène Marcovici. A decentralised diagnosis method with probabilistic cellular automata. Cellular Automata and Discrete Complex Systems. AUTOMATA 2023, Aug 2023, Trieste (Italy), France. ⟨10.1007/978-3-031-42250-8_5⟩. ⟨hal-04094230⟩
58 View
25 Download

Altmetric

Share

Gmail Facebook X LinkedIn More