Robust Self-stabilizing Clustering Algorithm [chapter]

Colette Johnen, Le Huy Nguyen
<span title="">2006</span> <i title="Springer Berlin Heidelberg"> <a target="_blank" rel="noopener" href="" style="color: black;">Lecture Notes in Computer Science</a> </i> &nbsp;
Ad hoc networks consist of wireless hosts that communicate with each other in the absence of a fixed infrastructure. Such network cannot rely on centralized and organized network management. The clustering problem consists in partitioning network nodes into groups called clusters, thus giving at the network a hierarchical organization. A self-stabilizing algorithm, regardless of the initial system state, converges in finite time to a set of states that satisfy a legitimacy predicate without
more &raquo; ... rnal intervention. Due to this property, self-stabilizing algorithms tolerate transient faults. In this paper we present a robust self-stabilizing clustering algorithm for ad hoc network. The robustness property guarantees that, starting from an arbitrary state, in one round, network is partitioned into clusters. After that, network stays partitioned during the convergence toward a legitimate configuration where the clusters partition is optimal. Résumé Les réseaux ad hoc se composent d'hôtes qui communiquent les uns avec les autres en l'absence d'une infrastructure fixe. A eux de prendre en charge l'organisation du réseau (routage, gestion de la bande passante, connectivité). Un tel réseau ne peut pas compter sur la connectivité centralisée et organisée. Le problème clustering consisteà partitionner les noeuds d'un réseau en grappes, donć etablissant une organisation hiérarchique au réseau. Un algorithme auto-stabilisant, indépendant de l'état initial du système, convergeà un ensemble de l'état qui satisfaità un prédicat légitime dans un temps fini, sans intervention externe. Grâceà cette propriété, les algorithmes auto-stabilisants tolèrent les défaillances transitoires. Dans cet article nous présentons un algorithme auto-stabilisant robuste d'agrégation pour les réseaux ad hoc. La propriété de robustesse garantit que,à partir d'uń etat arbitraire, en un round, le réseau est partitionné en grappes. Après, le systèmeévolue pour converger vers une configuration légitime où la partition est optimale. An ad hoc network is a self-organized network especially one with wireless or temporary plug-in connections. Such a network may operate in a standalone fashion, or may be connected to the larger Internet [12] . In Latin, ad hoc literally means "for this", further meaning "for this purpose only" and thus usually "temporary". Mobile routers may move randomly; thus, the network's topology may change rapidly and unpredictably. Such network cannot rely on centralized and organized network management. Significant examples include establishing survivable, efficient, dynamic communication for emergency/rescue operations, disaster relief efforts, and military networks. The meeting where participant will create a temporary wireless ad hoc network is also a typical example. Minimal configuration and quick deployment are needed in these situations. Clustering means partitioning network nodes into groups called clusters, giving to the network a hierarchical organization. A cluster is a connected graph composed of a clusterhead and (possibly) some ordinary nodes. Each node belongs to only one cluster. In addition, a cluster is required to obey to certain constraints that are used for network management, routing methods, resource allocation, etc. By dividing the network into non-overlapped clusters, intra-cluster routing is administered by the clusterhead and inter-cluster routing can be done in reactive manner by clusterhead leaders and gateway. Clustering has the following advantages. First, clustering facilitates the reuse of resource, which can improve the system capacity. Members of a cluster can share resources such as software, memory space, printer, etc, thus increasing its disposability and its accessibility. Secondly, clustering-based routing reduces the amount of routing information propagated in the network. Finally, clustering can be used to reduce the amount of information that is used to store the network state. The clusterhead will collect the state of nodes in its cluster and built an overview of its cluster state. Distant nodes outside of the cluster usually do not need to know the details of specific events occurring inside the cluster. Hence, an overview of the cluster's state is sufficient for those distant nodes to make control decisions.
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="">doi:10.1007/11945529_29</a> <a target="_blank" rel="external noopener" href="">fatcat:2cpkb6l5pfcw3lit6vcfbdmwhu</a> </span>
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