19 research outputs found
Gathering in Dynamic Rings
The gathering problem requires a set of mobile agents, arbitrarily positioned
at different nodes of a network to group within finite time at the same
location, not fixed in advanced.
The extensive existing literature on this problem shares the same fundamental
assumption: the topological structure does not change during the rendezvous or
the gathering; this is true also for those investigations that consider faulty
nodes. In other words, they only consider static graphs. In this paper we start
the investigation of gathering in dynamic graphs, that is networks where the
topology changes continuously and at unpredictable locations.
We study the feasibility of gathering mobile agents, identical and without
explicit communication capabilities, in a dynamic ring of anonymous nodes; the
class of dynamics we consider is the classic 1-interval-connectivity.
We focus on the impact that factors such as chirality (i.e., a common sense
of orientation) and cross detection (i.e., the ability to detect, when
traversing an edge, whether some agent is traversing it in the other
direction), have on the solvability of the problem. We provide a complete
characterization of the classes of initial configurations from which the
gathering problem is solvable in presence and in absence of cross detection and
of chirality. The feasibility results of the characterization are all
constructive: we provide distributed algorithms that allow the agents to
gather. In particular, the protocols for gathering with cross detection are
time optimal. We also show that cross detection is a powerful computational
element.
We prove that, without chirality, knowledge of the ring size is strictly more
powerful than knowledge of the number of agents; on the other hand, with
chirality, knowledge of n can be substituted by knowledge of k, yielding the
same classes of feasible initial configurations
Gracefully Degrading Gathering in Dynamic Rings
Gracefully degrading algorithms [Biely \etal, TCS 2018] are designed to
circumvent impossibility results in dynamic systems by adapting themselves to
the dynamics. Indeed, such an algorithm solves a given problem under some
dynamics and, moreover, guarantees that a weaker (but related) problem is
solved under a higher dynamics under which the original problem is impossible
to solve. The underlying intuition is to solve the problem whenever possible
but to provide some kind of quality of service if the dynamics become
(unpredictably) higher.In this paper, we apply for the first time this approach
to robot networks. We focus on the fundamental problem of gathering a squad of
autonomous robots on an unknown location of a dynamic ring. In this goal, we
introduce a set of weaker variants of this problem. Motivated by a set of
impossibility results related to the dynamics of the ring, we propose a
gracefully degrading gathering algorithm
Gracefully Degrading Gathering in Dynamic Rings
Gracefully degrading algorithms [Biely \etal, TCS 2018] are designed to circumvent impossibility results in dynamic systems by adapting themselves to the dynamics. Indeed, such an algorithm solves a given problem under some dynamics and, moreover, guarantees that a weaker (but related) problem is solved under a higher dynamics under which the original problem is impossible to solve. The underlying intuition is to solve the problem whenever possible but to provide some kind of quality of service if the dynamics become (unpredictably) higher.In this paper, we apply for the first time this approach to robot networks. We focus on the fundamental problem of gathering a squad of autonomous robots on an unknown location of a dynamic ring. In this goal, we introduce a set of weaker variants of this problem. Motivated by a set of impossibility results related to the dynamics of the ring, we propose a gracefully degrading gathering algorithm
Gracefully Degrading Gathering in Dynamic Rings
Gracefully degrading algorithms [Biely \etal, TCS 2018] are designed to circumvent impossibility results in dynamic systems by adapting themselves to the dynamics. Indeed, such an algorithm solves a given problem under some dynamics and, moreover, guarantees that a weaker (but related) problem is solved under a higher dynamics under which the original problem is impossible to solve. The underlying intuition is to solve the problem whenever possible but to provide some kind of quality of service if the dynamics become (unpredictably) higher.In this paper, we apply for the first time this approach to robot networks. We focus on the fundamental problem of gathering a squad of autonomous robots on an unknown location of a dynamic ring. In this goal, we introduce a set of weaker variants of this problem. Motivated by a set of impossibility results related to the dynamics of the ring, we propose a gracefully degrading gathering algorithm
Fault-Tolerant Dispersion of Mobile Robots
We consider the mobile robot dispersion problem in the presence of faulty
robots (crash-fault). Mobile robot dispersion consists of robots in
an -node anonymous graph. The goal is to ensure that regardless of the
initial placement of the robots over the nodes, the final configuration
consists of having at most one robot at each node. In a crash-fault setting, up
to robots may fail by crashing arbitrarily and subsequently lose all
the information stored at the robots, rendering them unable to communicate. In
this paper, we solve the dispersion problem in a crash-fault setting by
considering two different initial configurations: i) the rooted configuration,
and ii) the arbitrary configuration. In the rooted case, all robots are placed
together at a single node at the start. The arbitrary configuration is a
general configuration (a.k.a. arbitrary configuration in the literature) where
the robots are placed in some clusters arbitrarily across the graph. For
the first case, we develop an algorithm solving dispersion in the presence of
faulty robots in rounds, which improves over the previous
-round result by \cite{PS021}. For the
arbitrary configuration, we present an algorithm solving dispersion in
rounds, when the number of edges
and the maximum degree of the graph is known to the robots