Self-stabilizing Robots in Highly Dynamic Environments

Bournat, Marjorie; Datta, Ajoy K.; Dubois, Swan

doi:10.1007/978-3-319-49259-9_5

Cited by 11 publications

(11 citation statements)

References 29 publications

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“…The exploration with termination of interval connected rings has been studied in [16]. For rings that are connected over time, a perpetual self-stabilizing exploration algorithm has been proposed in [6]. Finally, the gathering problem on interval connected rings has been studied in [17].…”

Section: Related Workmentioning

confidence: 99%

Patrolling on Dynamic Ring Networks

Das

Luna

Gąsieniec

2019

SOFSEM 2019: Theory and Practice of Computer Science

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We study the problem of patrolling the nodes of a network collaboratively by a team of mobile agents, such that each node of the network is visited by at least one agent once in every I(n) time units, with the objective of minimizing the idle time I(n). While patrolling has been studied previously for static networks, we investigate the problem on dynamic networks with a fixed set of nodes, but dynamic edges. In particular, we consider 1-interval-connected ring networks and provide various patrolling algorithms for such networks, for k = 2 or k > 2 agents. We also show almost matching lower bounds that hold even for the best starting configurations. Thus, our algorithms achieve close to optimal idle time. Further, we show a clear separation in terms of idle time, for agents that have prior knowledge of the dynamic networks compared to agents that do not have such knowledge. This paper provides the first known results for collaborative patrolling on dynamic graphs.

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Section: Related Workmentioning

confidence: 99%

Patrolling on Dynamic Ring Networks

Das

Luna

Gąsieniec

2019

SOFSEM 2019: Theory and Practice of Computer Science

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“…In this section, we present our formal model. This model is borrowed from the one of [4] that proposes an extension of the classical model of robot networks in static graphs introduced in [21] to the context of dynamic graphs.…”

Section: Modelmentioning

confidence: 99%

“…The first attempt to solve exploration in the most general dynamicity scenario (i.e., connectedover-time assumption) has been proposed in [4]. The authors provide a protocol that deterministically solves the perpetual exploration problem.…”

Section: Introductionmentioning

confidence: 99%

“…According to the impossibility result in [10], we restrict this study to the FSYN C model. As we do not consider self-stabilization (contrarily to [4]), we assume that no pair of robots have a common initial location. Moreover, to ensure that the problem is not trivially solved in the initial configuration, we consider that, k, the number of robots, is strictly smaller than n, the number of nodes of the dynamic graph.…”

Section: Introductionmentioning

confidence: 99%

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Computability of Perpetual Exploration in Highly Dynamic Rings

Bournat¹,

Dubois²,

Petit³

2017

2017 IEEE 37th International Conference on Distributed Computing Systems (ICDCS)

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We consider systems made of autonomous mobile robots evolving in highly dynamic discrete environment i.e., graphs where edges may appear and disappear unpredictably without any recurrence, stability, nor periodicity assumption. Robots are uniform (they execute the same algorithm), they are anonymous (they are devoid of any observable ID), they have no means allowing them to communicate together, they share no common sense of direction, and they have no global knowledge related to the size of the environment. However, each of them is endowed with persistent memory and is able to detect whether it stands alone at its current location. A highly dynamic environment is modeled by a graph such that its topology keeps continuously changing over time. In this paper, we consider only dynamic graphs in which nodes are anonymous, each of them is infinitely often reachable from any other one, and such that its underlying graph (i.e., the static graph made of the same set of nodes and that includes all edges that are present at least once over time) forms a ring of arbitrary size.In this context, we consider the fundamental problem of perpetual exploration: each node is required to be infinitely often visited by a robot. This paper analyzes the computability of this problem in (fully) synchronous settings, i.e., we study the deterministic solvability of the problem with respect to the number of robots. We provide three algorithms and two impossibility results that characterize, for any ring size, the necessary and sufficient number of robots to perform perpetual exploration of highly dynamic rings.

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“…On the other hand, few algorithms have been designed for robots evolving in dynamic graphs. The majority of them deals with the problem of exploration [3,4,11,13,18] (robots must visit each node of the graph at least once or infinitely often depending on the variant of the problem). In the most related work to ours [19], Di Luna et al study the gathering problem in dynamic rings.…”

Section: Introductionmentioning

confidence: 99%

Gracefully Degrading Gathering in Dynamic Rings

Bournat¹,

Dubois²,

Petit³

2018

Lecture Notes in Computer Science

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Gracefully degrading algorithms [Biely et al., 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. arXiv:1805.05137v2 [cs.DC] 2 Aug 2018 Rules for Phase K K 1 :: AllButT woW aitingW alker() −→ InitiateWalk() K 2 :: W aitingW alker() −→ StopMoving() K 3 :: ∃r ∈ N odeM ate(), P otentialM inOrSearcherW ithM inW aiting(r ) −→ BecomeWaitingWalker(r') K 4 :: ∃r ∈ N odeM ate(), RighterW ithM inW aiting(r ) ∧ ExistsEdge(right, current) −→ BecomeAwareSearcher(r') Rules for Phase M M 1 :: P otentialM inOrRighter() ∧ M inDiscovery() −→ BecomeMinWaitingWalker(r) M 2 :: ∃r ∈ N odeM ate(), N otW alkerW ithHeadW alker(r ) ∧ ExistsEdge(right, current) −→ BecomeAwareSearcher(r') M 3 :: ∃r ∈ N odeM ate(), N otW alkerW ithHeadW alker(r ) −→ BecomeAwareSearcher(r'); StopMoving() M 4 :: ∃r ∈ N odeM ate(), N otW alkerW ithT ailW alker(r ) −→ BecomeTailWalker(r'); Walk() M 5 :: ∃r ∈ N odeM ate(), P otentialM inW ithAwareSearcher(r ) −→ BecomeAwareSearcher(r'); Search() M 6 :: AllButOneRighter() −→ InitiateSearch() M 7 :: ∃r ∈ N odeM ate(), RighterW ithSearcher(r ) −→ BecomeAwareSearcher(r'); Search() M 8 :: P otentialM inOrRighter() −→ MoveRight() M 9 :: DumbSearcherM inRevelation() −→ BecomeAwareSearcher(r); Search() M 10 :: ∃r ∈ N odeM ate(), DumbSearcherW ithAwareSearcher(r ) −→ BecomeAwareSearcher(r'); Search() M 11 :: Searcher() −→ Search()

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Self-stabilizing Robots in Highly Dynamic Environments

Cited by 11 publications

References 29 publications

Patrolling on Dynamic Ring Networks

Patrolling on Dynamic Ring Networks

Computability of Perpetual Exploration in Highly Dynamic Rings

Gracefully Degrading Gathering in Dynamic Rings

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