Lowest-ID with adaptive ID reassignment: a novel mobile ad-hoc networks clustering algorithm

D. Gavalas, G. Pantziou, C. Konstantopoulos, B. Mamalis
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引用次数: 36

Abstract

Clustering is a promising approach for building hierarchies and simplifying the routing process in mobile ad-hoc network environments. The main objective of clustering is to identify suitable node representatives, i.e. cluster heads (CHs), to store routing and topology information and maximize clusters stability. Traditional clustering algorithms suggest CH election exclusively based on node IDs or location information and involve frequent broadcasting of control packets, even when network topology remains unchanged. More recent works take into account additional metrics (such as energy and mobility) and optimize initial clustering. However, in many situations (e.g. in relatively static topologies) re-clustering procedure is hardly ever invoked; hence initially elected CHs soon reach battery exhaustion. Herein, we introduce an efficient distributed clustering algorithm that uses both mobility and energy metrics to provide stable cluster formations. CHs are initially elected based on the time and cost-efficient lowest-ID method. During clustering maintenance phase though, node IDs are re-assigned according to nodes mobility and energy status, ensuring that nodes with low-mobility and sufficient energy supply are assigned low IDs and, hence, are elected as CHs. Our algorithm also reduces control traffic volume since broadcast period is adjusted according to the nodes mobility pattern: we employ infrequent broadcasting for relative static network topologies, and increase broadcast frequency for highly mobile network configurations. Simulation results verify that energy consumption is uniformly distributed among network nodes and that signaling overhead is significantly decreased.
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具有自适应ID重分配的最低ID:一种新的移动自组织网络聚类算法
在移动自组织网络环境中,聚类是一种很有前途的构建层次结构和简化路由过程的方法。聚类的主要目标是识别合适的节点代表,即簇头(CHs),以存储路由和拓扑信息,并最大限度地提高簇的稳定性。传统的聚类算法建议仅基于节点id或位置信息进行CH选举,并且涉及频繁广播控制数据包,即使网络拓扑保持不变。最近的工作考虑了额外的指标(如能量和移动性),并优化了初始聚类。然而,在许多情况下(例如在相对静态的拓扑中),几乎不会调用重新聚类过程;因此,最初选出的CHs很快就会耗尽电池。在此,我们引入了一种高效的分布式聚类算法,该算法使用移动性和能量度量来提供稳定的聚类形成。CHs最初是根据时间和成本效益最低的id方法选出的。在集群维护阶段,根据节点的移动性和能量状态重新分配节点id,确保低移动性和能量供应充足的节点被分配低id,从而被选为CHs。我们的算法还减少了控制流量,因为广播周期根据节点的移动模式进行调整:对于相对静态的网络拓扑,我们采用不频繁的广播,而对于高度移动的网络配置,我们增加了广播频率。仿真结果表明,能量消耗在网络节点间分布均匀,信令开销显著降低。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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