一种新的基于节点性能的manet聚类算法

M. Dyabi, A. Hajami, H. Allali
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引用次数: 7

摘要

移动自组织网络(MANET)是一种动态无线网络,它可以在不需要任何预先存在的基础设施的情况下形成,其中每个节点都可以充当路由器。与固定网络相比,随着manet的引入,出现了一些新的安全问题。特别是安全路由,是一个重要而复杂的问题。将移动节点组织到可管理的集群中可以限制安全路由信息的数量。在集群结构下,移动节点由称为簇头的节点管理。簇头角色消耗资源,因为它总是处于开启状态,并且负责远程传输。如果一个节点有这个角色,它会迅速消耗它的资源,并且在它死亡后,它的所有成员都将是无头的。在这项工作中,我们提出了一种新的自组织网络聚类算法,其中簇围绕最强大的节点形成,即具有最佳物质资源(如剩余能量,空闲内存,处理器速度和硬盘空间)的节点被选为簇头。我们的算法通过在计算中包含节点密度来考虑节点范围。因此,我们确信簇头是由网络中最强大、移动最少的节点代表的,它可以在最佳条件下发挥其作用。此解决方案将用于管理自组织网络中的加密密钥。我们的算法在选出簇头的数量和性能方面进行了重大改进。分析和仿真结果表明了本文算法的性能,并与文献中其他聚类算法进行了比较。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
A new MANETs clustering algorithm based on nodes performances
A mobile ad hoc network (MANET) is a dynamic wireless network which can be formed without the need for any pre-existing infrastructure in which each node can act as a router. In comparison with fixed networks, some new security issues have arisen with the introduction of MANETs. Secure routing, in particular, is an important and complicated issue. Organizing mobile nodes into manageable clusters can limit the amount of secure routing information. Under a cluster structure, mobile nodes are managed by nodes called cluster heads. The clusterhead role is resource consuming since its always switched on and is responsible for the long-range transmission. If a node has this role, it would burn its resource quickly, and after it died, all its members would be headless. In this work, we propose a new clustering algorithm for ad hoc networks, where the clusters are formed around the most powerful nodes, i.e, the node that has the best material resources such as residual energy, free memory, processor speed and hard disk space is elected as cluster head. Our algorithm takes into account the node range by including in our calculation the node density. Therefore, we are sure that the cluster head is represented by the most powerful and the less mobile node in the network that can perform its roles in the best conditions. This solution will be used to manage cryptographic keys in an ad hoc network. Our algorithm gives major improvements regarding the number and the performance of elected cluster heads. Analysis and simulation results are used to show the performance of our algorithm, compared with other clustering algorithms in literature.
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