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Brief Announcement: Fault-Tolerant Shape Formation in the Amoebot Model

Authors Irina Kostitsyna , Christian Scheideler , Daniel Warner

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Author Details

Irina Kostitsyna
  • Department of Mathematics and Computer Science, Eindhoven University of Technology, The Netherlands
Christian Scheideler
  • Department of Computer Science, Paderborn University, Germany
Daniel Warner
  • Department of Computer Science, Paderborn University, Germany

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Irina Kostitsyna, Christian Scheideler, and Daniel Warner. Brief Announcement: Fault-Tolerant Shape Formation in the Amoebot Model. In 1st Symposium on Algorithmic Foundations of Dynamic Networks (SAND 2022). Leibniz International Proceedings in Informatics (LIPIcs), Volume 221, pp. 23:1-23:3, Schloss Dagstuhl - Leibniz-Zentrum für Informatik (2022)


The amoebot model is a distributed computing model of programmable matter. It envisions programmable matter as a collection of computational units called amoebots or particles that utilize local interactions to achieve tasks of coordination, movement and conformation. In the geometric amoebot model the particles operate on a hexagonal tessellation of the plane. Within this model, numerous problems such as leader election, shape formation or object coating have been studied. One area that has not received much attention so far, but is highly relevant for a practical implementation of programmable matter, is fault tolerance. The existing literature on that aspect allows particles to crash but assumes that crashed particles do not recover. We propose a new model in which a crash causes the memory of a particle to be reset and a crashed particle can detect that it has crashed and try to recover using its local information and communication capabilities. We propose an algorithm that solves the hexagon shape formation problem in our model if a finite number of crashes occur and a designated leader particle does not fail. At the heart of our solution lies a fault-tolerant implementation of the spanning forest primitive, which, since other algorithms in the amoebot model also make use of it, is also of general interest.

Subject Classification

ACM Subject Classification
  • General and reference → General conference proceedings
  • Programmable matter
  • Geometric amoebot model
  • Fault tolerance
  • Shape formation


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  1. Zahra Derakhshandeh, Robert Gmyr, Andréa W Richa, Christian Scheideler, and Thim Strothmann. An algorithmic framework for shape formation problems in self-organizing particle systems. In Proceedings of the Second Annual International Conference on Nanoscale Computing and Communication, pages 1-2, 2015. Google Scholar
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