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Clustering algorithm based on nature-inspired approach for energy optimization in heterogeneous wireless sensor network
Applied Soft Computing ( IF 5.472 ) Pub Date : 2021-02-13 , DOI: 10.1016/j.asoc.2021.107171
Israel Edem Agbehadji; Richard C. Millham; Abdultaofeek Abayomi; Jason J. Jung; Simon James Fong; Samuel Ofori Frimpong

In this paper, we present a clustering model for energy optimization based on the nature-inspired behaviour of animals. This clustering model finds the optimal distance to send data packets from one location to another, either long or short distances, so as to maintain the lifetime of the sensor network. The challenge with sensor networks is how to balance the energy load, which can be achieved by selecting a sensor node with an adequate amount of energy from a cluster to compensate for those sensor nodes with limited amount of energy. Generally, the clustering technique is one of the approaches to solve this challenge because it optimizes energy to increase the lifetime of the sensor network. We focus on nodes with different energy makeup, and based on the number of nodes that send packets, and evaluated the network performance in terms of the stability period, network lifetime and network throughput. Two nature-inspired algorithms (that is, kestrel-based search algorithm and wolf search algorithm with minus step previous) were compared to evaluate which one is energy-efficient when used as a clustering algorithm. It was found that, the Kestrel-based Search Algorithm Distributed Energy Efficient Clustering (KSA-DEEC) model has the optimal network run time (in seconds) to send a higher number of packets to base station successfully. Consequently, The KSA-DEEC model has an optimal network lifetime performance as compared to the Wolf Search Algorithm with Minus Step Previous (WSAMP)-DEEC model. It also has the highest network throughput in the simulation that was performed while the WSAMP-DEEC model showed prospects of better performance in some of the cases.



中文翻译:

基于自然启发方法的异构无线传感器网络能量优化聚类算法

在本文中,我们提出了一种基于动物自然行为的能量优化聚类模型。该聚类模型找到了将数据包从一个位置发送到另一个位置的最佳距离(长距离或短距离),以保持传感器网络的生命周期。传感器网络面临的挑战是如何平衡能量负载,这可以通过从群集中选择具有足够能量的传感器节点来补偿能量有限的那些传感器节点来实现。通常,聚类技术是解决此难题的方法之一,因为它可以优化能量以增加传感器网络的寿命。我们专注于具有不同能量组成的节点,并根据发送数据包的节点数量,并从稳定期,网络生存期和网络吞吐量方面评估了网络性能。比较了两种受自然启发的算法(即基于红est的搜索算法和先减负号的Wolf搜索算法),以评估哪种算法在用作聚类算法时是节能的。结果发现,基于Kestrel的搜索算法分布式节能聚类(KSA-DEEC)模型具有最佳的网络运行时间(以秒为单位),可以成功地向基站发送更多的数据包。因此,与带有减步前的Wolf搜索算法(WSAMP)-DEEC模型相比,KSA-DEEC模型具有最佳的网络生命周期性能。

更新日期:2021-02-21
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