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Shuffled Complex Evolution Approach for Load Balancing of Gateways in Wireless Sensor Networks

机译:无线传感器网络载荷平衡的播放复杂的演化方法

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摘要

Energy consumption is one of the important factor of Wireless Sensor Networks (WSN). It has much attention in many fields. From recent studies, it is observed that energy consumption in WSN is challenging task as the energy is limited resource. This energy is needed for sensor nodes operation. In order to maximize the network lifetime, energy consumption should be mitigated. In cluster based WSN, cluster head i.e., the leader of cluster performs various activities, such as data collection from its member nodes, data aggregation and data exchange with base station. Hence, load balancing in WSNs is one of the challenging task to maximize network lifetime. In order to address this problem, in this paper, Shuffled Complex Evolution (SCE) algorithm is used. A novel fitness function is also designed to evaluate fitness of solutions produced by SCE algorithm. In SCE, the solutions with best and worst fitness value exchange their information to produce new off-spring. We have simulated proposed load balancing algorithm along with other state-of-the-art load balancing algorithms, namely Node Local Density Load Balancing, Score Based Load Balancing, Simple Genetic Algorithm based load balancing, Novel Genetic Algorithm based Load Balancing. It is observed from experimental results that proposed load balancing algorithm outperforms state-of-the-art load balancing algorithms in terms of load balancing, energy consumption, execution time, number of sensor nodes and number of heavy loaded sensor nodes.
机译:能量消耗是无线传感器网络(WSN)的重要因素之一。它在许多领域都有很多关注。从最近的研究中,由于能量是有限的资源,因此观察到WSN中的能量消耗是具有挑战性的任务。传感器节点操作需要这种能量。为了最大限度地提高网络寿命,应减轻能源消耗。在基于群集的WSN,群集头中,群集的领导者执行各种活动,例如从其成员节点,数据聚合和与基站的数据交换的数据收集。因此,WSN中的负载平衡是最大化网络生命周期的具有挑战性的任务之一。为了解决这个问题,在本文中,使用了播放的复杂进化(SCE)算法。一种新颖的健身功能,还旨在评估SCE算法生产的解决方案的适应性。在SCE中,具有最佳和最糟糕的健康价值的解决方案交换了他们的信息以产生新的春季新春。我们已经模拟了提出的负载平衡算法以及其他最先进的负载平衡算法,即节点局部密度负载平衡,基于SIST的负载均衡,基于简单的遗传算法的负载均衡,基于新的基于遗传算法的负载均衡。从实验结果观察到,所提出的负载平衡算法在负载平衡,能量消耗,执行时间,传感器节点数量和重载传感器节点的数量方面优于最先进的负载平衡算法。

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