On the runtime of universal coating for programmable matter

Zahra Derakhshandeh, Robert Gmyr, Alexandra Porter, Andrea Richa, Christian Scheideler, Thim Strothmann

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

17 Scopus citations


Imagine coating buildings and bridges with smart particles (also coined smart paint) that monitor structural integrity and sense and report on traffic and wind loads, leading to technology that could do such inspection jobs faster and cheaper and increase safety at the same time.In this paper, we study the problem of uniformly coating objects of arbitrary shape in the context of self-organizing programmable matter, i.e., the programmable matter consists of simple computational elements called particles that can establish and release bonds and can actively move in a self-organized way.Particles are anonymous, have constant-size memory and utilize only local interactions in order to coat an object.We continue the study of our Universal Coating algorithm by focusing on its runtime analysis, showing that our algorithm terminates within a linear number of rounds with high probability.We also present a matching linear lower bound that holds with high probability.We use this lower bound to show a linear lower bound on the competitive gap between fully local coating algorithms and coating algorithms that rely on global information, which implies that our algorithm is also optimal in a competitive sense.Simulation results show that the competitive ratio of our algorithm may be better than linear in practice.

Original languageEnglish (US)
Title of host publicationDNA Computing and Molecular Programming - 22nd International Conference, DNA 2016, Proceedings
EditorsYannick Rondelez, Damien Woods
PublisherSpringer Verlag
Number of pages17
ISBN (Print)9783319439938
StatePublished - 2016
Event22nd International Conference on Computing and Molecular Programming, DNA 2016 - Munich, Germany
Duration: Sep 4 2016Sep 8 2016

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume9818 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


Other22nd International Conference on Computing and Molecular Programming, DNA 2016

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science(all)


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