Leader election and shape formation with self-organizing programmable matter

Zahra Derakhshandeh, Robert Gmyr, Thim Strothmann, Rida Bazzi, Andrea Richa, Christian Scheideler

Research output: Chapter in Book/Report/Conference proceedingConference contribution

20 Scopus citations

Abstract

In this paper we consider programmable matter consisting of simple computational elements, called particles, that can establish and release bonds and can actively move in a self-organized way, and we investigate the feasibility of solving fundamental problems relevant for programmable matter. As a model for such self-organizing particle systems, we will use a generalization of the geometric amoebot model first proposed in [21]. Based on the geometric model, we present efficient localcontrol algorithms for leader election and line formation requiring only particles with constant size memory, and we also discuss the limitations of solving these problems within the general amoebot model.

Original languageEnglish (US)
Title of host publicationLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
PublisherSpringer Verlag
Pages117-132
Number of pages16
Volume9211
ISBN (Print)9783319219981
DOIs
StatePublished - 2015
Event21st International Conference on DNA Computing and Molecular Programming, 2015 - Boston and Cambridge, United States
Duration: Aug 17 2015Aug 21 2015

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume9211
ISSN (Print)03029743
ISSN (Electronic)16113349

Other

Other21st International Conference on DNA Computing and Molecular Programming, 2015
CountryUnited States
CityBoston and Cambridge
Period8/17/158/21/15

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ASJC Scopus subject areas

  • Computer Science(all)
  • Theoretical Computer Science

Cite this

Derakhshandeh, Z., Gmyr, R., Strothmann, T., Bazzi, R., Richa, A., & Scheideler, C. (2015). Leader election and shape formation with self-organizing programmable matter. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 9211, pp. 117-132). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 9211). Springer Verlag. https://doi.org/10.1007/978-3-319-21999-8_8