一种基于权重的移动自组网聚类方案

A. Bentaleb, S. Harous, Abdelhak Boubetra
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引用次数: 22

摘要

移动自组织网络(manet)是一种自组织、自配置的多跳无线网络,没有任何预先存在的通信基础设施或集中管理。manet的可扩展性是一个新问题,当网络拓扑包含大量节点,并且在有限的无线带宽和节点移动性下需要大量数据包时,导致无线链路故障的高频率。manet中的聚类是将大型网络划分为多个子网的一个重要课题,广泛应用于有效的网络管理、改进资源管理、分层路由协议设计、服务质量和良好的manet安全监控体系结构。随后,人们提出了许多聚类方法,将节点划分成集群,以支持路由和网络管理。本文提出了一种新的基于权值的聚类算法。它考虑了信任(T)、密度(D)、流动性(M)和能量(E)等指标,在集群形成阶段局部选择最优簇头。在我们提出的算法中,每个集群由其簇头监督,以确保可接受的安全级别。它旨在提高带宽等稀缺资源的利用率,以最少的集群数量保持稳定的集群结构,降低集群形成和维护的总开销,最大限度地提高网络中移动节点的寿命,降低能耗。通过初步的仿真实验,比较了该算法在CHs的平均数量、CH的平均变化数量、重新隶属关系的总数量、聚类稳定性和总开销方面与最低ID、最高程度和WCA的性能。初步结果表明,基于所考虑的性能指标,我们的方案比其他聚类方案执行得更好。
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A Weight Based Clustering Scheme for Mobile Ad hoc Networks
Mobile Ad hoc networks (MANETs) are self-organizing and self-configuring multi-hop wireless networks without any pre-existing communication infrastructures or centralized management. Scalability in MANETs is a new issue where network topology includes large number of nodes and demands a large number of packets in limited wireless bandwidth and nodes mobility that results in a high frequency of failure regarding wireless links. Clustering in MANETs is an important topic that divides the large network into several sub networks and widely used in efficient network management, improving resource management, hierarchical routing protocol design, Quality of Service and a good monitoring architecture of MANETs security. Subsequently, many clustering approaches have been proposed to divide nodes into clusters to support routing and network management. In this paper, we propose a new efficient weight based clustering algorithm. It takes into consideration the metrics: trust (T), density (D), Mobility (M) and energy (E) to choose locally the optimal cluster heads during cluster formation phase. In our proposed algorithm each cluster is supervised by its cluster head in order to ensure an acceptable level of security. It aims to improve the usage of scarce resources such as bandwidth, maintaining stable clusters structure with a lowest number of clusters formed, decreasing the total overhead during cluster formation and maintenance, maximizing lifespan of mobile nodes in the network and reduces energy consumption. Preliminary simulation experiments are conducted to compare the performance of our algorithm to Lowest ID, Highest Degree and WCA in terms of Average Number of CHs, Average Number of CH Changes, Total Number of Re-affiliations, Clusters Stability and Total Overhead. The initial results show that our scheme performs better than other clustering schemes based on the performance metrics considered.
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