利用模糊聚类拓扑延长WSN网络生存期

Trong-The Nguyen, Chin-Shiuh Shieh, Thi-Kien Dao, Jaw-Shyang Wu, Wu-Chih Hu
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引用次数: 16

摘要

适当的聚类是延长无线传感器网络生命周期的有效途径。大多数提出的聚类算法都没有考虑基站的位置。这将导致多跳无线传感器网络中的热点问题。本文采用模糊聚类拓扑来延长无线传感器网络的寿命。考虑剩余能量、与基站的距离以及传感器节点邻近参数的影响,采用模糊聚类拓扑对簇头半径进行调整。这有助于减少靠近基站或电池电量较低的传感器节点的集群内流量负载。利用模糊逻辑处理簇头半径估计中的不确定性。我们的方法与文献中一些流行的算法进行了比较,包括LEACH, Gupta和CHEF。我们的方法在各种性能指标中执行,例如第一个节点死亡(FND),一半节点存活(HNA),最后一个节点死亡(LND)和能效指标。仿真结果表明,该方法在某些情况下的准确率可达54%,优于其他算法。因此,该方法是一种稳定、高效的聚类算法,可应用于任何实际的WSN应用。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Prolonging of the Network Lifetime of WSN Using Fuzzy Clustering Topology
Adequate clustering provides an effective way for prolonging the lifetime of a wireless sensor network (WSN). Most proposed clustering algorithms do not consider the location of the base station. This will lead to the hot spots problem in multi-hop WSNs. In this paper, a fuzzy clustering topology is employed to prolong the lifetime of WSNs. Considering the residual energy, the distances to the base station and influence of neighboring parameters of the sensor nodes, the cluster-head radius are adjusted by fuzzy clustering topology. This helps to decrease the intra-cluster traffic load of sensor nodes closer to the base station or having lower battery level. The uncertainties in the estimation of cluster-head radius are handled by fuzzy logic. Our approach is compared with some popular algorithms in literature, including LEACH, Gupta and CHEF. Our approach performs in various performance metrics, such as First Node Dies (FND), Half of the Nodes Alive (HNA), Last Node Dies (LND) and energy-efficiency metrics. Simulation results show that the proposed method performs better than the other algorithms, up to 54% in certain cases. Therefore, this method is a stable and energy-efficient clustering algorithm can be applied to any real-world WSN applications.
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