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首页> 外文期刊>Parallel and Distributed Systems, IEEE Transactions on >Overlapping Multihop Clustering for Wireless Sensor Networks
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Overlapping Multihop Clustering for Wireless Sensor Networks

机译:无线传感器网络的重叠多跳群集

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摘要

Clustering is a standard approach for achieving efficient and scalable performance in wireless sensor networks. Traditionally, clustering algorithms aim at generating a number of disjoint clusters that satisfy some criteria. In this paper, we formulate a novel clustering problem that aims at generating overlapping multihop clusters. Overlapping clusters are useful in many sensor network applications, including intercluster routing, node localization, and time synchronization protocols. We also propose a randomized, distributed multihop clustering algorithm (KOCA) for solving the overlapping clustering problem. KOCA aims at generating connected overlapping clusters that cover the entire sensor network with a specific average overlapping degree. Through analysis and simulation experiments, we show how to select the different values of the parameters to achieve the clustering process objectives. Moreover, the results show that KOCA produces approximately equal-sized clusters, which allow distributing the load evenly over different clusters. In addition, KOCA is scalable; the clustering formation terminates in a constant time regardless of the network size.
机译:群集是在无线传感器网络中实现高效和可扩展性能的标准方法。传统上,聚类算法旨在生成满足某些条件的许多不相交的聚类。在本文中,我们提出了一个新的聚类问题,旨在生成重叠的多跳聚类。重叠群集在许多传感器网络应用程序中很有用,包括群集间路由,节点本地化和时间同步协议。我们还提出了一种用于解决重叠聚类问题的随机,分布式多跳聚类算法(KOCA)。 KOCA旨在生成连接的重叠簇,这些簇以特定的平均重叠度覆盖整个传感器网络。通过分析和仿真实验,我们展示了如何选择参数的不同值来实现聚类过程的目标。此外,结果表明,KOCA产生了近似相等大小的群集,这允许将负载均匀地分布在不同的群集上。此外,KOCA具有可扩展性。无论网络大小如何,集群形成都将在固定时间内终止。

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