Polylogarithmic Time Algorithms for Shortest Path Forests in Programmable Matter
Andreas Padalkin, Christian Scheideler

TL;DR
This paper introduces polylogarithmic time distributed algorithms for computing shortest path forests in the geometric amoebot model of programmable matter, enabling efficient path computations for multiple sources and destinations.
Contribution
It presents two novel distributed algorithms for shortest path forests in the amoebot model, achieving polylogarithmic time complexity for various configurations.
Findings
Shortest path tree for a single source in O(log ℓ) rounds
Shortest path forest for multiple sources in O(log n log^2 k) rounds
O(1) rounds for single pair shortest path problem
Abstract
In this paper, we study the computation of shortest paths within the \emph{geometric amoebot model}, a commonly used model for programmable matter. Shortest paths are essential for various tasks and therefore have been heavily investigated in many different contexts. For example, in the programmable matter context, which is the focus of this paper, Kostitsyna et al. have utilized shortest path trees to transform one amoebot structure into another [DISC, 2023]. We consider the \emph{reconfigurable circuit extension} of the model where this amoebot structure is able to interconnect amoebots by so-called circuits. These circuits permit the instantaneous transmission of simple signals between connected amoebots. We propose two distributed algorithms for the \emph{shortest path forest problem} where, given a set of sources and a set of destinations, the amoebot structure has to…
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Taxonomy
TopicsElectricity Theft Detection Techniques · Smart Grid Security and Resilience · Optimal Power Flow Distribution
