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DUCF: Distributed load balancing Unequal Clustering in wireless sensor networks using Fuzzy approach

机译:DUCF:使用模糊方法的无线传感器网络中的分布式负载均衡不均等聚类

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Data gathering in wireless sensor networks (WSN) consumes more energy due to large amount of data transmitted. In direct transmission (DT) method, each node has to transmit its generated data to the base station (BS) which leads to higher energy consumption and affects the lifetime of the network. Clustering is one of the efficient ways of data gathering in WSN. There are various kinds of clustering techniques, which reduce the overall energy consumption in sensor networks. Cluster head (CH) plays a vital role in data gathering in clustered WSN. Energy consumption in CH node is comparatively higher than other non CH nodes because of its activities like data aggregation and transmission to BS node. The present day clustering algorithms in WSN use multi-hopping mechanism which cost higher energy for the CH nodes near to BS since it routes the data from other CHs to BS. Some CH nodes may die earlier than its intended lifetime due to its overloaded work which affects the performance of the WSN. This paper contributes a new clustering algorithm, Distributed Unequal Clustering using Fuzzy logic (DUCF) which elects CHs using fuzzy approach. DUCF forms unequal clusters to balance the energy consumption among the CHs. Fuzzy inference system (FIS) in DUCF uses the residual energy, node degree and distance to BS as input variables for CH election. Chance and size are the output fuzzy parameters in DUCF. DUCF assigns the maximum limit (size) of a number of member nodes for a CH by considering its input fuzzy parameters. The smaller cluster size is assigned for CHs which are nearer to BS since it acts as a router for other distant CHs. DUCF ensures load balancing among the clusters by varying the cluster size of its CH nodes. DUCF uses Mamdani method for fuzzy inference and Centroid method for defuzzification. DUCF performance was compared with well known algorithms such as LEACH, CHEF and EAUCF in various network scenarios. The experimental results indicated that DUCF forms unequal clusters which ensure load balancing among clusters, which again improves the network lifetime compared with its counterparts. (C) 2015 Elsevier B.V. All rights reserved.
机译:无线传感器网络(WSN)中收集的数据由于传输大量数据而消耗更多能量。在直接传输(DT)方法中,每个节点必须将其生成的数据传输到基站(BS),这导致更高的能耗并影响网络的寿命。集群是WSN中有效的数据收集方式之一。有各种各样的群集技术,可以减少传感器网络中的总体能耗。群集头(CH)在群集WSN中的数据收集中起着至关重要的作用。由于CH节点的活动(例如数据聚合和向BS节点的传输),其能量消耗相对高于其他非CH节点。目前,WSN中的聚类算法使用多跳机制,因为它会将数据从其他CH路由到BS,从而使靠近BS的CH节点的能量消耗更高。由于其过载的工作会影响WSN的性能,某些CH节点可能会早于其预期寿命而死亡。本文提出了一种新的聚类算法,即使用模糊逻辑(DUCF)的分布式不等式聚类,该算法使用模糊方法选择CH。 DUCF形成不平等的集群,以平衡CH之间的能耗。 DUCF中的模糊推理系统(FIS)使用剩余能量,节点度和到BS的距离作为CH选举的输入变量。机会和大小是DUCF中的输出模糊参数。 DUCF通过考虑CH的输入模糊参数,为其分配多个成员节点的最大限制(大小)。较小的群集大小被分配给更接近BS的CH,因为它充当其他遥远CH的路由器。 DUCF通过更改其CH节点的群集大小来确保群集之间的负载平衡。 DUCF使用Mamdani方法进行模糊推理,并使用质心方法进行去模糊。在各种网络情况下,将DUCF性能与LEACH,CHEF和EAUCF等著名算法进行了比较。实验结果表明,DUCF形成不相等的群集,从而确保了群集之间的负载平衡,与同类产品相比,它再次提高了网络寿命。 (C)2015 Elsevier B.V.保留所有权利。

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