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Energy-Efficient Fuzzy-Logic-Based Clustering Technique for Hierarchical Routing Protocols in Wireless Sensor Networks

机译:无线传感器网络中基于节能逻辑的分层路由协议聚类技术

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摘要

In wireless sensor networks, the energy source is limited to the capacity of the sensor node’s battery. Clustering in WSN can help with reducing energy consumption because transmission energy is related to the distance between sender and receiver. In this paper, we propose a fuzzy logic model for cluster head election. The proposed model uses five descriptors to determine the opportunity for each node to become a CH. These descriptors are: residual energy, location suitability, density, compacting, and distance from the base station. We use this fuzzy logic model in proposing the Fuzzy Logic-based Energy-Efficient Clustering for WSN based on minimum separation Distance enforcement between CHs (FL-EEC/D). Furthermore, we adopt the Gini index to measure the clustering algorithms’ energy efficiency in terms of their ability to balance the distribution of energy through WSN sensor nodes. We compare the proposed technique FL-EEC/D with a fuzzy logic-based CH election approach, a k-means based clustering technique, and LEACH. Simulation results show enhancements in energy efficiency in terms of network lifetime and energy consumption balancing between sensor nodes for different network sizes and topologies. Results show an average improvement in terms of first node dead and half nodes dead.
机译:在无线传感器网络中,能源仅限于传感器节点的电池容量。 WSN中的群集可以帮助减少能耗,因为传输能量与发送方和接收方之间的距离有关。在本文中,我们提出了一种模糊逻辑模型,用于簇头的选择。所提出的模型使用五个描述符来确定每个节点成为CH的机会。这些描述符是:剩余能量,位置适合性,密度,压实度以及与基站的距离。我们使用这种模糊逻辑模型,基于CH之间的最小距离强制实施(FL-EEC / D),为WSN提出了基于模糊逻辑的WSN节能聚类。此外,我们采用基尼系数来衡量聚类算法的能量效率,以衡量它们通过WSN传感器节点平衡能量分配的能力。我们将提出的技术FL-EEC / D与基于模糊逻辑的CH选择方法,基于k均值的聚类技术和LEACH进行了比较。仿真结果表明,针对不同的网络规模和拓扑,在网络寿命和传感器节点之间的能耗平衡方面,能源效率得到了提高。结果显示,在第一个节点失效和半个节点失效方面,平均改进。

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