Efficient D-2-D via Leader Election: Arbitrary Initial Configuration and No Global Knowledge

TL;DR

This paper presents a distributed algorithm for mobile agents to solve the Distance-2-Dispersion problem from arbitrary initial configurations on anonymous graphs, also achieving leader election without prior global knowledge.

Contribution

It introduces a novel algorithm that solves D-2-D and leader election starting from arbitrary configurations with no global knowledge, in near-optimal rounds.

Findings

Solves D-2-D in O(max{n log^2 n, m}) rounds with O(log n) memory per agent.

Achieves leader election in the same asymptotic time and memory bounds.

Works on arbitrary initial configurations without prior graph knowledge.

Abstract

Distance-2-Dispersion (D-2-D) problem aims to disperse $k$ mobile agents starting from an arbitrary initial configuration on an anonymous port-labeled graph $G$ with $n$ nodes such that no two agents occupy adjacent nodes in the final configuration, though multiple agents may occupy a single node if there is no other empty node whose all adjacent nodes are also empty. In the existing literature, this problem is solved starting from a rooted configuration for $k$ $(\geq 1)$ agents in $O(m\Delta)$ synchronous rounds with a total of $O(\log n)$ memory per agent, where $m$ is the number of edges and $\Delta$ is the maximum degree of the graph. In this work we study the D-2-D problem using $n$ mobile agents starting from an arbitrary initial configuration. Solving D-2-D with $n$ agents is equivalent to finding a maximal independent set of the graph as size of any maximal independent set must be less than $n$. We solve this problem and terminate in $O(max\{n\log^2 n, m\})$ rounds using $O(\log n)$ memory per agent. The agents do not have any prior knowledge of any graph parameters. During the run of our algorithm, we also solve the leader election problem that elects an agent as a leader in $O(max\{n\log^2 n, m\})$ rounds with $O(\log n)$ bits of memory at each agent without requiring any prior global knowledge.