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Improving the performance of hierarchical wireless sensor networks using the metaheuristic algorithms: efficient cluster head selection
Sensor Review ( IF 1.6 ) Pub Date : 2021-08-05 , DOI: 10.1108/sr-03-2021-0094
Farzad Kiani 1 , Amir Seyyedabbasi 2 , Sajjad Nematzadeh 3
Affiliation  

Purpose

Efficient resource utilization in wireless sensor networks is an important issue. Clustering structure has an important effect on the efficient use of energy, which is one of the most critical resources. However, it is extremely vital to choose efficient and suitable cluster head (CH) elements in these structures to harness their benefits. Selecting appropriate CHs and finding optimal coefficients for each parameter of a relevant fitness function in CHs election is a non-deterministic polynomial-time (NP-hard) problem that requires additional processing. Therefore, the purpose of this paper is to propose efficient solutions to achieve the main goal by addressing the related issues.

Design/methodology/approach

This paper draws inspiration from three metaheuristic-based algorithms; gray wolf optimizer (GWO), incremental GWO and expanded GWO. These methods perform various complex processes very efficiently and much faster. They consist of cluster setup and data transmission phases. The first phase focuses on clusters formation and CHs election, and the second phase tries to find routes for data transmission. The CH selection is obtained using a new fitness function. This function focuses on four parameters, i.e. energy of each node, energy of its neighbors, number of neighbors and its distance from the base station.

Findings

The results obtained from the proposed methods have been compared with HEEL, EESTDC, iABC and NR-LEACH algorithms and are found to be successful using various analysis parameters. Particularly, I-HEELEx-GWO method has provided the best results.

Originality/value

This paper proposes three new methods to elect optimal CH that prolong the networks lifetime, save energy, improve overhead along with packet delivery ratio.



中文翻译:

使用元启发式算法提高分层无线传感器网络的性能:高效的簇头选择

目的

无线传感器网络中的有效资源利用是一个重要问题。集群结构对能源的有效利用具有重要影响,能源是最关键的资源之一。然而,在这些结构中选择高效且合适的簇头 (CH) 元素来利用它们的好处是极其重要的。在 CHs 选举中选择合适的 CHs 并为相关适应度函数的每个参数找到最佳系数是一个非确定性多项式时间 (NP-hard) 问题,需要额外的处理。因此,本文的目的是通过解决相关问题提出有效的解决方案来实现主要目标。

设计/方法/方法

本文从三种基于元启发式的算法中汲取灵感;灰狼优化器 (GWO)、增量 GWO 和扩展 GWO。这些方法非常有效和快速地执行各种复杂的过程。它们包括集群设置和数据传输阶段。第一阶段侧重于集群的形成和CHs的选举,第二阶段试图寻找数据传输的路由。CH 选择是使用新的适应度函数获得的。该函数关注四个参数,即每个节点的能量、其邻居的能量、邻居的数量以及它与基站的距离。

发现

从所提出的方法获得的结果已经与 HEEL、EESTDC、iABC 和 NR-LEACH 算法进行了比较,并且发现使用各种分析参数是成功的。特别是I-HEELEx-GWO方法提供了最好的结果。

原创性/价值

本文提出了三种选择最优 CH 的新方法,这些方法可以延长网络寿命、节省能源、提高开销以及数据包传递率。

更新日期:2021-08-05
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