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Adaptive Self-Organization in Anonymous Dynamic Networks

Authors Garrett Parzych , Joshua J. Daymude

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Subject Classification

ACM Subject Classification
  • Theory of computation → Distributed algorithms
  • Networks → Network algorithms
Keywords
  • Dynamic networks
  • anonymous nodes
  • broadcast
  • self-organization
  • biological distributed algorithms

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Abstract

We introduce the problem of adaptive self-organization in which the nodes of an anonymous, synchronous dynamic network must distributively change the collective distribution of their responses (or "colors") as a function of time-varying environmental signals, even when these signals are only perceived locally and the network topology changes adversarially. Specifically, a signal adversary may change the type of signal and which node(s) witness that signal arbitrarily between rounds. If a signal (or lack thereof) s persists in the system for sufficiently long, the dynamic network must stabilize such that nodes' colors closely approximate r(s), a goal distribution defined by the problem instance. By symmetry, deterministic nodes can only hope to solve homogeneous instances of adaptive self-organization, i.e., those in which all nodes stabilize with the same color. We present a linear-time, logarithmic-memory, deterministic algorithm for this class of instances that works even when the multiplicity and location of signal witnesses change arbitrarily. We then give a randomized extension of this algorithm that solves arbitrary (i.e., not necessarily homogeneous) instances of adaptive self-organization with high probability in the same time and space bounds.

Cite As Get BibTex

Garrett Parzych and Joshua J. Daymude. Adaptive Self-Organization in Anonymous Dynamic Networks. In 40th International Symposium on Distributed Computing (DISC 2026). Leibniz International Proceedings in Informatics (LIPIcs), Volume 397, pp. 27:1-27:20, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2026) https://doi.org/10.4230/LIPIcs.DISC.2026.27

Author Details

Garrett Parzych
  • School of Computing and Augmented Intelligence, Biodesign Center for Biocomputing, Security and Society, Arizona State University, Tempe, AZ, USA
Joshua J. Daymude
  • School of Computing and Augmented Intelligence, Biodesign Center for Biocomputing, Security and Society, Arizona State University, Tempe, AZ, USA

Funding

This work is supported in part by National Science Foundation award CCF-2312537.

Acknowledgements

J.J.D. thanks Annie Daymude, Monty Carson, and Julie Carson for the many hours of childcare that created the working time to complete this paper.

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