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Performance analysis of iterative linear regression-based clustering in wireless sensor networks

机译:无线传感器网络中迭代线性回归基于播出的性能分析

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摘要

Clustering, an energy efficient approach is preferred in wireless sensor network due to the limited energy consumption of sensor nodes. Clustering saves energy as the communication is restricted to a few nodes, thereby increasing the network lifetime. Recently, an iterative linear regression-based clustering is proposed to improve the cluster quality in a wireless sensor network. The quality of clusters so obtained is evaluated through Matlab using the cluster validity analysis platform. However, the performance of the clusters so obtained using iterative linear regression in terms of energy efficiency is not dealt. Hence, an attempt is made to investigate the performance of the network through real time experiments. The network performances are studied in terms of intra-cluster and inter-cluster energy consumption, first node death, half node death and last node death. To prove the effectiveness of the proposed technique, it is compared with the standard k-means clustering algorithm. The results reveal that the clusters obtained using iterative linear regression is efficient in enhancing the lifetime of the wireless sensor network.
机译:由于传感器节点的能耗有限,聚类,在无线传感器网络中优选节能方法。群集节省能量,因为通信限制为几个节点,从而增加了网络生命周期。最近,提出了一种迭代线性回归基于基于群集,以改善无线传感器网络中的集群质量。使用群集有效性分析平台通过MATLAB评估如此获得的簇的质量。然而,在能效方面使用迭代线性回归如此获得的簇的性能。因此,尝试通过实时实验来研究网络的性能。在集群内和群集间能耗,第一节线死亡,半节点死亡和最后一个节点死亡方面研究了网络性能。为了证明所提出的技术的有效性,将其与标准K-Means聚类算法进行比较。结果表明,使用迭代线性回归获得的簇是增强无线传感器网络的寿命的有效。

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