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Diffusing-CRN k-means: an improved k-means clustering algorithm applied in cognitive radio ad hoc networks

机译:扩散CRN k均值:一种应用于认知无线电ad hoc网络的改进的k均值聚类算法

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摘要

With increasing demand of new wireless applications and increasing number of wireless user's, problem of spectrum scarcity arises. In this context, cognitive radio supports dynamic spectrum access to address spectrum scarcity problem. Cognitive radio defined the cognitive radio nodes by their ability to intelligently adapt the environment to achieve specific objectives through advanced techniques. The variance of channel availability for cognitive radio nodes degrades connectivity and robustness of this type of network; in this case the use of clustering is an effective approach to meet this challenge. Indeed, the geographical areas are homogeneous in terms of type of radio spectrum, radio resources are better allocated by grouping cognitive radio nodes per cluster. Clustering is interesting to effectively manage the spectrum or routing in cognitive radio ad hoc networks. In this paper, we aim to improve connectivity and cooperativeness of cognitive radio nodes based on the improvement of the k-means algorithm. Our proposed algorithm is applied in cognitive radio ad hoc networks. The obtained results in terms of exchange messages and execution time show the feasibility of our algorithm to form clusters in order to improve connectivity and cooperativeness of cognitive radio nodes in the context of cognitive radio ad hoc networks.
机译:随着对新无线应用的需求增加以及无线用户数量的增加,出现了频谱稀缺的问题。在这种情况下,认知无线电支持动态频谱访问以解决频谱稀缺问题。认知无线电通过认知无线电节点通过先进技术智能适应环境以实现特定目标的能力来定义认知无线电节点。认知无线电节点的信道可用性差异会降低此类网络的连接性和鲁棒性。在这种情况下,使用群集是应对这一挑战的有效方法。实际上,就无线电频谱的类型而言,地理区域是同质的,可以通过对每个集群的认知无线电节点进行分组来更好地分配无线电资源。聚类对于有效管理认知无线电自组织网络中的频谱或路由很有趣。在本文中,我们旨在基于k-means算法的改进来提高认知无线电节点的连通性和协作性。我们提出的算法被应用于认知无线电自组织网络。在交换消息和执行时间方面获得的结果表明,我们的算法形成簇的可行性,以改善认知无线电自组织网络中认知无线电节点的连接性和协作性。

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