Self-Stabilizing Construction of Bounded Size Clusters

Johnen, Colette; Nguyen, Le Huy

doi:10.1109/ispa.2008.43

Cited by 17 publications

(29 citation statements)

References 15 publications

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“…We are placing in the context of clustering protocols where nodes proclaim themselves leaders like [1,2,6,11,15,17,19,20,23], and not in the context of protocols where leaders are nominated by other nodes like [5,9]. The general form of the original silent self-stabilizing weight-based 1-hop clustering protocol P is described in Protocol 1.…”

Section: The Original Protocol Pmentioning

confidence: 99%

“…The computations of original protocol are however modified to ensure the service guarantee to T P protocol despite HT D disruptions. Protocols GDMAC [1], building a kfold dominating set, and BSC [19] building a capacitated dominating set, are transformed respectively to R-GDMAC [20], and R-BSC [17] using our transformer. The status transition diagram of a node v is illustrated in Figure 1, where transitions are the rules executed by v (and defined in Protocol 3).…”

Section: Input Variablesmentioning

confidence: 99%

“…Clustering is found very attractive in infrastructure-less networks, like ad-hoc networks, since it limits the responsibility of network management only to leaders, and it allows the use of hierarchical routing. This is why numerous clustering protocols were proposed in the literature [1,2,6,11,15,17,19,20,21,23].…”

Section: Introductionmentioning

confidence: 99%

“…Hence, in weight-based clustering protocols, leaders are chosen according to their weight value in order to be the most suitable nodes in their clusters. Protocols proposed in [1,6,16,17,19,20] are weight-based.…”

Section: Introductionmentioning

confidence: 99%

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From Self- to Self-stabilizing with Service Guarantee 1-hop Weight-Based Clustering

Johnen

Mekhaldi

2012

Lecture Notes in Computer Science

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Abstract. We propose a transformer building a silent self-stabilizing with service guarantee 1-hop clustering protocol T P of an input silent self-stabilizing 1-hop clustering protocol P. From an arbitrary configuration, T P reaches a safe configuration in at most 3 rounds, where the following useful minimal service is provided: "each node belongs to a 1-hop cluster having an effective leader". During stabilization of T P, the minimal service is preserved, so the clustering structure is available throughout the entire network. The minimal service is also maintained despite the occurrences of some external disruptions, called highly tolerated disruptions, denoted HT D. T P reaches a terminal (also legitimate) configuration in at most 4 * SP rounds where SP is the stabilization time of P protocol. Moreover, T P requires only 2 bits per node more than P.

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Section: The Original Protocol Pmentioning

confidence: 99%

Section: Input Variablesmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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From Self- to Self-stabilizing with Service Guarantee 1-hop Weight-Based Clustering

Johnen

Mekhaldi

2012

Lecture Notes in Computer Science

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“…Many 1-hop clustering algorithms have been proposed in the literature. A large number of them are self-stabilizing [2], [7], [10], [6], [9], [15], [19]. There are also robust self-stabilizing clustering algorithms [16], [13], and selfstabilizing with safe convergence [18].…”

Section: Introductionmentioning

confidence: 99%

Self-Stabilizing Computation and Preservation of Knowledge of Neighbor Clusters

Johnen

Mekhaldi

2011

2011 IEEE Fifth International Conference on Self-Adaptive and Self-Organizing Systems

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Abstract-The area of self-stabilization in large scale networks has been received increasing attention among researchers, since self-stabilization provides a foundation for self-properties, including self-healing, self-organizing and self-adaptive. This paper makes contributions in two areas. First, we describe a new extended approach of selfstabilization, called self-stabilization with service guarantee. Second, we propose a self-stabilizing protocol computing and preserving the knowledge of neighbor clusters, called CNK. A cluster-head maintains about each neighbor cluster: the identity of its head, paths leading to it, and the list of members. The most interesting property of CNK is the service guarantee during the stabilization phase. CNK quickly provides, in at most 4 rounds, the following minimal useful service: "each cluster-head knows valid paths leading to heads of all its neighbor clusters". CNK protocol preserves the minimal service despite changes in the clustering structure (creation of new clusters, restructuring or crumbling of existing clusters). The knowledge of neighbor clusters is thus highly available. This knowledge is enough to allow the continuity functioning of hierarchical protocols as hierarchical routing protocols.

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An SDN approach to route massive data flows of sensor networks

Flauzac

Santamaria

Nolot

et al. 2020

Int J Communication

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With the advent of the Internet of Things (IoT), more and more devices can establish a connection with local area networks and use routing protocols to forward all information to the sink. But these devices may not have enough resources to execute a complex routing protocol or to memorize all information about the network. With proactive routing protocols, each node calculates the best path, and it needs enough resources to memorize the network topology.With reactive routing protocols, each node has to broadcast the message to learn the right path that the packets must follow. In all cases, in large networks such as IoT, this is not an appropriate mechanism. This paper presents a new software-defined network (SDN)-based network architecture to optimize the resource consumption of each IoT object while securing the exchange of messages between the embedded devices. In this architecture, the controller is in charge of all decisions, and objects only exchange messages and forward packets among themselves. In the case of large networks, the network is organized into clusters. Our proposed network architectures are tested with 1000 things grouped in five clusters and managed by one SDN controller. The tests using OpenDayLight and IoT embedded applications have been implemented on several scenarios providing the ability and the scalability from dynamic reorganization of the end-devices. This approach explores the network performance issues using a virtualized SDN-clustered environment which contributes to a new model for future network architectures. KEYWORDScomputer network management, distributed computing, Internet of Things, network topology, software-defined networking, wireless sensor networks INTRODUCTIONWith the Internet of Things (IoT), we have more and more devices connected to our local area network. Many of these things are only sensor devices to control humidity or the temperature of a room or to measure atmospheric pressure to predict the weather. Each of these devices is connected to the local area network of an enterprise or on the metropolitan network of a town. Sometimes, these things involve remote control as with thermostats or exchange of information between themselves as with smoke detection systems. Our goal with each thing is not only to route information on a network but also to execute orders of some kind. With the new paradigm of software-defined networking (SDN), 1,2 being one of the major research subjects obtaining support from a large number of major industries, we can decouple the control Int J Commun Syst. 2020;33:e4309.wileyonlinelibrary.com/journal/dac

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Self-Stabilizing Construction of Bounded Size Clusters

Abstract: Abstract

Cited by 17 publications

References 15 publications

From Self- to Self-stabilizing with Service Guarantee 1-hop Weight-Based Clustering

From Self- to Self-stabilizing with Service Guarantee 1-hop Weight-Based Clustering

Self-Stabilizing Computation and Preservation of Knowledge of Neighbor Clusters

An SDN approach to route massive data flows of sensor networks

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