LIPIcs.OPODIS.2023.7.pdf
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Energy-Constrained Programmable Matter Under Unfair Adversaries
Authors
Jamison W. Weber 
,
Tishya Chhabra ,
Andréa W. Richa ,
Joshua J. Daymude
-
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Volume:
27th International Conference on Principles of Distributed Systems (OPODIS 2023)
Series: Leibniz International Proceedings in Informatics (LIPIcs)
Conference: International Conference on Principles of Distributed Systems (OPODIS) - License:
Creative Commons Attribution 4.0 International license
- Publication Date: 2024-01-18
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Author Details
- School of Computing and Augmented Intelligence, Arizona State University, Tempe, AZ, USA
- School of Computing and Augmented Intelligence, Arizona State University, Tempe, AZ, USA
- School of Computing and Augmented Intelligence & Biodesign Center for Biocomputing, Security and Society, Arizona State University, Tempe, AZ, USA
Cite AsGet BibTex
Jamison W. Weber, Tishya Chhabra, Andréa W. Richa, and Joshua J. Daymude. Energy-Constrained Programmable Matter Under Unfair Adversaries. In 27th International Conference on Principles of Distributed Systems (OPODIS 2023). Leibniz International Proceedings in Informatics (LIPIcs), Volume 286, pp. 7:1-7:21, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2024)
https://doi.org/10.4230/LIPIcs.OPODIS.2023.7
https://doi.org/10.4230/LIPIcs.OPODIS.2023.7
Abstract
Individual modules of programmable matter participate in their system’s collective behavior by expending energy to perform actions. However, not all modules may have access to the external energy source powering the system, necessitating a local and distributed strategy for supplying energy to modules. In this work, we present a general energy distribution framework for the canonical amoebot model of programmable matter that transforms energy-agnostic algorithms into energy-constrained ones with equivalent behavior and an 𝒪(n²)-round runtime overhead - even under an unfair adversary - provided the original algorithms satisfy certain conventions. We then prove that existing amoebot algorithms for leader election (ICDCN 2023) and shape formation (Distributed Computing, 2023) are compatible with this framework and show simulations of their energy-constrained counterparts, demonstrating how other unfair algorithms can be generalized to the energy-constrained setting with relatively little effort. Finally, we show that our energy distribution framework can be composed with the concurrency control framework for amoebot algorithms (Distributed Computing, 2023), allowing algorithm designers to focus on the simpler energy-agnostic, sequential setting but gain the general applicability of energy-constrained, asynchronous correctness.
Subject Classification
ACM Subject Classification
- Theory of computation → Distributed algorithms
- Theory of computation → Self-organization
Keywords
- Programmable matter
- amoebot model
- energy distribution
- concurrency
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