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arXiv 0908.3715cs.NI
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无线传感器网络中的重叠多跳分簇算法

Overlapping Multi-hop Clustering for Wireless Sensor Networks

Moustafa Youssef, Adel Youssef, Mohamed Younis

摘要:

分簇是无线传感器网络中实现高效和可扩展性能的标准方法。传统的聚类算法的目标是生成若干满足一定条件的不相交聚类。在本文中,我们制定了一个新的聚类问题,旨在产生重叠的多跳集群。重叠簇在许多传感器网络应用中是有用的,包括簇间路由、节点定位和时间同步协议。我们还提出了一个随机的,分布式多跳聚类算法(KOCA)解决重叠聚类问题。KOCA的目标是生成覆盖整个传感器网络的连接重叠集群,具有特定的平均重叠度。通过分析和仿真实验说明了如何选择不同的参数值来实现聚类过程的目标。此外,结果表明,KOCA产生近似相等大小的集群,这使得负载均匀分布在不同的集群。此外,KOCA是可扩展的;无论网络大小如何,聚类形成都会在恒定的时间内终止。

英文摘要:

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 multi-hop clusters. Overlapping clusters are useful in many sensor network applications, including inter-cluster routing, node localization, and time synchronization protocols. We also propose a randomized, distributed multi-hop 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 allows 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.

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

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