Deterministic Leader Election for Stationary Programmable Matter with Common Direction

Jérémie Chalopin, Shantanu Das, Maria Kokkou · arXiv · 2024

With a common agreed direction but no chirality, the paper shows deterministic stationary leader election is possible in the Amoebot model, while explicit termination is impossible.

Moderate AI ConfidenceGood SourceSimulationReadiness Unknown

Plain English summary

Leader election is a basic building block for many tasks in programmable matter systems. This work studies leader election in the Amoebot model on a triangular grid, focusing on connected configurations that include obstacles (nodes particles cannot move to). The authors assume particles share a common direction (the horizontal axis) but do not agree on the other grid directions (no chirality). They first show that an algorithm with explicit termination cannot work under these assumptions. They then provide an implicitly terminating algorithm that elects exactly one leader without requiring any movement. The paper contrasts this with a different setting (chirality without direction agreement), where explicit termination is possible but the number of leaders can depend on the initial configuration’s symmetry.

Why this matters

It provides a stationary, deterministic, unique leader election approach under common-direction agreement (and no chirality) in a setting with obstacles, where explicit termination is impossible and prior stationary deterministic results were limited. No evidence of implementation, testing, or deployment is provided in the abstract.

Key findings

  • In the Amoebot model with obstacles and common-direction agreement but no chirality, explicit termination for leader election is not possible.
  • An implicitly terminating algorithm can elect a unique leader without requiring any movement.
  • The results differ from the common model with chirality but no direction agreement, where explicit termination is possible but leader count depends on symmetry.
  • Directional agreement enables unique stationary deterministic leader election beyond previously known simply connected cases under a sequential scheduler.

Limitations

The abstract does not provide experimental validation, performance metrics, or details of the algorithm beyond its stationary/implicit termination properties; it is framed within the Amoebot model and specific directional/chirality assumptions.

Publication

Publisher
arXiv
Publication date
February 16, 2024
Research type
Preprint
arXiv
2402.10582
Access
open

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