Partial gathering of mobile agents in dynamic rings

In this paper, we consider the partial gathering problem of mobile agents in synchronous dynamic bidirectional ring networks. The partial gathering problem is a generalization of the (well-investigated) total gathering problem, which requires that all k agents distributed in the network terminate at a non-predetermined single node. The partial gathering problem requires, for a given positive integer g ( < k ), that agents terminate in a configuration such that either at least g agents or no agent exists at each node. When k ≥ 2 g , the requirement for the partial gathering problem is strictly weaker than that for the total gathering problem, and thus it is interesting to clarify the difference in the move complexity between them. So far, the partial gathering problem has been considered in static graphs. In this paper, we start considering partial gathering in dynamic graphs. As a first step, we consider this problem in 1-interval connected rings, that is, one of the links in a ring may be missing at each time step. In such networks, focusing on the relationship between the values of k and g , we fully characterize the solvability of the partial gathering problem and analyze the move complexity of the proposed algorithms when the problem can be solved. First, we show that the g -partial gathering problem is unsolvable when k ≤ 2 g . Second, we show that the problem can be solved with O ( n log g ) time and the total number of O ( gn log g ) moves when 2 g + 1 ≤ k ≤ 3 g − 2 . Third, we show that the problem can be solved with O ( n ) time and the total number of O ( kn ) moves when 3 g − 1 ≤ k ≤ 8 g − 4 . Notice that since k = O ( g ) holds when 3 g − 1 ≤ k ≤ 8 g − 4 , the move complexity O ( kn ) in this case can be represented also as O ( gn ). Finally, we show that the problem can be solved with O ( n ) time and the total number of O ( gn ) moves when k ≥ 8 g − 3 . These results mean that the partial gathering problem can be solved also in dynamic rings when k ≥ 2 g + 1 . In addition, agents require a total number of Ω( gn ) (resp., Ω( kn )) moves to solve the partial (resp., total) gathering problem. Thus, when k ≥ 3 g − 1 , agents can solve the partial gathering problem with the asymptotically optimal total number of O ( gn ) moves, which is strictly smaller than that for the total gathering problem.

Authors

Institutions

Publication Details

Journal
Theoretical Computer Science
Published
2025-12-09
DOI
https://doi.org/10.1016/j.tcs.2025.115684
Primary Topic
Optimization and Search Problems
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Partial gathering of mobile agents in dynamic rings

Junya Nakamura, Yuichi Sudo, Yonghwan Kim, Masahiro Shibata
Theoretical Computer Science
Optimization and Search Problems
article

Partial gathering of mobile agents in dynamic rings

Junya Nakamura, Yuichi Sudo, Yonghwan Kim, Masahiro Shibata
article en

Abstract

In this paper, we consider the partial gathering problem of mobile agents in synchronous dynamic bidirectional ring networks. The partial gathering problem is a generalization of the (well-investigated) total gathering problem, which requires that all k agents distributed in the network terminate at a non-predetermined single node. The partial gathering problem requires, for a given positive integer g ( < k ), that agents terminate in a configuration such that either at least g agents or no agent exists at each node. When k ≥ 2 g , the requirement for the partial gathering problem is strictly weaker than that for the total gathering problem, and thus it is interesting to clarify the difference in the move complexity between them. So far, the partial gathering problem has been considered in static graphs. In this paper, we start considering partial gathering in dynamic graphs. As a first step, we consider this problem in 1-interval connected rings, that is, one of the links in a ring may be missing at each time step. In such networks, focusing on the relationship between the values of k and g , we fully characterize the solvability of the partial gathering problem and analyze the move complexity of the proposed algorithms when the problem can be solved. First, we show that the g -partial gathering problem is unsolvable when k ≤ 2 g . Second, we show that the problem can be solved with O ( n log g ) time and the total number of O ( gn log g ) moves when 2 g + 1 ≤ k ≤ 3 g − 2 . Third, we show that the problem can be solved with O ( n ) time and the total number of O ( kn ) moves when 3 g − 1 ≤ k ≤ 8 g − 4 . Notice that since k = O ( g ) holds when 3 g − 1 ≤ k ≤ 8 g − 4 , the move complexity O ( kn ) in this case can be represented also as O ( gn ). Finally, we show that the problem can be solved with O ( n ) time and the total number of O ( gn ) moves when k ≥ 8 g − 3 . These results mean that the partial gathering problem can be solved also in dynamic rings when k ≥ 2 g + 1 . In addition, agents require a total number of Ω( gn ) (resp., Ω( kn )) moves to solve the partial (resp., total) gathering problem. Thus, when k ≥ 3 g − 1 , agents can solve the partial gathering problem with the asymptotically optimal total number of O ( gn ) moves, which is strictly smaller than that for the total gathering problem.

Theoretical Computer ScienceVol. 1063
Toyohashi University of Technology (JP), Nagoya Institute of Technology (JP), Hosei University (JP), Kyushu Institute of Technology (JP)
Openalex Percentile: Top 56%
Optimization and Search Problems
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.