Abstract

This paper proposes a novel dynamic, distributive, and self-organizing entropy based clustering scheme that benefits from the local information of sensor nodes measured in terms of entropy and use that as criteria for cluster head election and cluster formation. It divides the WSN into two-levels of hierarchy and three-levels of energy heterogeneity of sensor nodes. The simulation results reveal that the proposed approach outperforms existing baseline algorithms in terms of energy consumption, stability period, and the network lifetime.

中文翻译：

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异构无线传感器网络的基于信息熵的聚类算法

摘要

本文提出了一种新颖的基于动态，分布和自组织熵的聚类方案，该方案受益于根据熵测得的传感器节点的局部信息，并将其用作聚类头选举和聚类形成的标准。它将WSN分为两级层次和三级传感器节点能量异质性。仿真结果表明，该方法在能耗，稳定期和网络寿命方面均优于现有的基准算法。