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首页> 外文期刊>Wireless personal communications: An Internaional Journal >A Congestion Aware, Energy Efficient, on Demand Fuzzy Logic Based Clustering Protocol for Multi-hop Wireless Sensor Networks
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A Congestion Aware, Energy Efficient, on Demand Fuzzy Logic Based Clustering Protocol for Multi-hop Wireless Sensor Networks

机译:用于多跳无线传感器网络的需求模糊逻辑基于需求的充血感知,节能,节能

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摘要

Node clustering or simply clustering provides an effective way of organizing the network to achieve energy efficiency, efficient distribution of workload, maintain a connected hierarchy and accomplish data aggregation. On the other hand multi-hop communication significantly reduces the energy required for communication and multi-path communication helps in distributing the relay load and it also increases reliability of the network. The proposed methodology in this paper is a combination of these three features. In addition to that, we have used fuzzy logic in cluster head (CH) selection to blend different parameters to select the set of best possible CHs and to handle the uncertainties in parametric quantities. There are five main aspects of the proposed algorithm (a) on demand based clustering (b) fuzzy logic based CH selection (c) dynamic unequal cluster range for CHs' (d) a cost function based multi-hop relay selections (e) Fibonacci sequence based relay load distribution. The proposed approach creates more balanced cluster by means of fuzzy logic and inequality in cluster range. It saves energy by conducting CH selection on demand basis. Finally, the cost function based multi-hop relay selections along with Fibonacci sequence based relay load distribution create an optimal structure for relaying data.The proposed approach is implemented and compared with the well known approaches like LEACH, ECPF, CHEF and UCR. Simulation results have shown that the proposed approach performs better, in every aspect of network life time and other metrics.
机译:节点群集或简单的群集提供了组织网络以实现能效,有效分布工作负载的有效方法,维护连接的层次结构并完成数据聚合。另一方面,多跳通信显着降低了通信所需的能量,并且多路通信在分配中继负载方面有助于增加网络的可靠性。本文提出的方法是这三种特征的组合。除此之外,我们还使用模糊逻辑在群集头(CH)选择中,混合不同的参数,以选择最佳可能的CHS并以参数批量处理不确定性。所提出的算法(a)的基于需求的聚类(b)模糊逻辑的CH选择(c)动态不等群集范围是CHS'(d)的算法(a)的五个主要方面是基于成本函数的多跳中继选择(e)fibonacci基于序列的继电器负载分布。该方法通过模糊逻辑和集群范围内的不等式创造了更平衡的集群。它通过按需求进行CH选择来节省能量。最后,基于成本函数的多跳中继选择以及基于Fibonacci序列的中继负载分布为中继数据的最佳结构产生了最佳结构。建议的方法是实现的,并将其与Leach,ECPF,厨师和UCR等众所周知的方法进行了比较。仿真结果表明,在网络生命时间和其他度量的各个方面,所提出的方法更好地表现更好。

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