Partial Gathering of Mobile Agents in Asynchronous Rings

TL;DR

This paper introduces the partial gathering problem for mobile agents in asynchronous rings, proposes three algorithms with optimal move complexities, and analyzes the differences from total gathering.

Contribution

It presents three algorithms for partial gathering in asynchronous rings, including deterministic with IDs, randomized anonymous, and a non-solvable case, analyzing their move complexities.

Findings

Deterministic algorithm achieves $O(gn)$ moves with unique IDs.

Randomized anonymous algorithm also achieves $O(gn)$ expected moves.

Some initial configurations are unsolvable without IDs, with lower bounds matching the algorithms.

Abstract

In this paper, we consider the partial gathering problem of mobile agents in asynchronous unidirectional rings equipped with whiteboards on nodes. The partial gathering problem is a new generalization of the total gathering problem. The partial gathering problem requires, for a given integer $g$, that each agent should move to a node and terminate so that at least $g$ agents should meet at the same node. The requirement for the partial gathering problem is weaker than that for the (well-investigated) total gathering problem, and thus, we have interests in clarifying the difference on the move complexity between them. We propose three algorithms to solve the partial gathering problem. The first algorithm is deterministic but requires unique ID of each agent. This algorithm achieves the partial gathering in $O(gn)$ total moves, where $n$ is the number of nodes. The second algorithm is randomized and requires no unique ID of each agent (i.e., anonymous). This algorithm achieves the partial gathering in expected $O(gn)$ total moves. The third algorithm is deterministic and requires no unique ID of each agent. For this case, we show that there exist initial configurations in which no algorithm can solve the problem and agents can achieve the partial gathering in $O(kn)$ total moves for solvable initial configurations, where $k$ is the number of agents. Note that the total gathering problem requires $\Omega (kn)$ total moves, while the partial gathering problem requires $\Omega (gn)$ total moves in each model. Hence, we show that the move complexity of the first and second algorithms is asymptotically optimal.