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首页> 外文期刊>International Journal of Computer Aided Engineering and Technology >Performance measures of fuzzy C-means algorithm in wireless sensor networks
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Performance measures of fuzzy C-means algorithm in wireless sensor networks

机译：无线传感器网络中模糊C均值算法的性能度量

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摘要

The major issues that govern performance of wireless sensor networks (WSNs) are efficient uses of limited resources and appropriate routing decisions of network paths under the severely constrained energy scenario. In this work, to address these issues uses of k-means and fuzzy C-means algorithms are investigated for clusters formation and subsequent selection of cluster heads (CHs). For all these newly formed clusters; selection of cluster head is done based on member sensor nodes residual energy status (RES) followed by estimation of Euclidean distances. Depending upon the Euclidean distance measures between the sink node and the estimated energy-centroid (EC) of clusters these clusters are classified into five types. The RES estimation is exercised for all the CHs and sensor nodes (SNs) of the network. Outcomes of simulation results indicate superior performance of fuzzy-c means algorithm compared to k-means algorithm. Further, a case study is presented, wherein the sink is allowed to have some movements in the service area. Here, different quadrant of service area exhibits different pattern of query spatial distribution. The optimal location of sink is sought to support energy efficient operational aspects of the WSNs.
机译：控制无线传感器网络（WSN）性能的主要问题是在资源严重受限的情况下有效利用有限的资源以及网络路径的适当路由决策。在这项工作中，为了解决这些问题，对k均值和模糊C均值算法的使用进行了研究，以进行聚类形成和随后选择聚类头（CH）。对于所有这些新形成的集群；根据成员传感器节点的剩余能量状态（RES）进行簇头的选择，然后估算欧几里得距离。根据接收节点和群集的估计能量质心（EC）之间的欧式距离度量，将这些群集分为五类。对网络的所有CH和传感器节点（SN）进行RES估计。仿真结果表明，与k-means算法相比，模糊c均值算法具有更好的性能。此外，提出了一个案例研究，其中允许水槽在服务区域内进行一些移动。在这里，服务区域的不同象限表现出不同的查询空间分布模式。寻求接收器的最佳位置以支持WSN的节能操作方面。

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