Hybridized Black Widow-Honey Badger Optimization: Swarm Intelligence Strategy for Node Localization Scheme in WSN

IF 3.6 2区计算机科学 Q2 COMPUTER SCIENCE, INFORMATION SYSTEMS

Journal of Grid Computing Pub Date : 2024-01-26 DOI:10.1007/s10723-024-09740-y

K Johny Elma, Praveena Rachel Kamala S, Saraswathi T

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引用次数: 0

Abstract

The evolutionary growth of Wireless Sensor Networks (WSN) exploits a wide range of applications. To deploy the WSN in a larger area, for sensing the environment, the accurate location of the node is a prerequisite. Owing to these traits, the WSN has been effectively implemented with devices. Using various localization techniques, the information related to node location is obtained for unknown nodes. Recently, node localization has employed the standard bio-inspired algorithm to sustain the fast convergence ability of WSN applications. Thus, this paper aims to develop a new hybrid optimization algorithm for solving the node localization problems among the unknown nodes in WSN. This hybrid optimization scheme is developed with two efficient heuristic strategies of Black Widow Optimization (BWO) and Honey Badger Algorithm (HBA), named as Hybridized Black Widow-Honey Badger Optimization (HBW-HBO) to achieve the objective of the framework. The main objective of the developed heuristic-based node localization framework is to minimize the localization error between the actual locations and detected locations of all nodes in WSN. For validating the developed heuristic-based node localization scheme in WSN, it is compared with different existing optimization strategies using different measures. The experimental analysis proves the robust and consistent node localization performance in WSN for the developed scheme than the other comparative algorithms.

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黑寡妇-蜜獾混合优化：用于 WSN 节点定位方案的蜂群智能策略

无线传感器网络（WSN）的不断发展带来了广泛的应用。要在更大的范围内部署 WSN 并感知环境，节点的精确定位是先决条件。由于这些特点，WSN 已通过设备有效地实现。利用各种定位技术，可以获得未知节点的相关位置信息。最近，节点定位采用了标准的生物启发算法，以维持 WSN 应用的快速收敛能力。因此，本文旨在开发一种新的混合优化算法，用于解决 WSN 中未知节点间的节点定位问题。这种混合优化方案采用了黑寡妇优化（Black Widow Optimization，BWO）和蜜獾算法（Honey Badger Algorithm，HBA）两种高效的启发式策略，命名为混合黑寡妇-蜜獾优化（Hybridized Black Widow-Honey Badger Optimization，HBW-HBO），以实现框架的目标。所开发的基于启发式的节点定位框架的主要目标是最大限度地减少 WSN 中所有节点的实际位置与检测位置之间的定位误差。为了验证所开发的基于启发式的 WSN 节点定位方案，使用不同的测量方法将其与现有的不同优化策略进行了比较。实验分析证明，与其他比较算法相比，所开发的方案在 WSN 中的节点定位性能稳健且一致。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

Journal of Grid Computing COMPUTER SCIENCE, INFORMATION SYSTEMS-COMPUTER SCIENCE, THEORY & METHODS

CiteScore

8.70

自引率

9.10%

发文量

审稿时长

>12 weeks

期刊介绍： Grid Computing is an emerging technology that enables large-scale resource sharing and coordinated problem solving within distributed, often loosely coordinated groups-what are sometimes termed "virtual organizations. By providing scalable, secure, high-performance mechanisms for discovering and negotiating access to remote resources, Grid technologies promise to make it possible for scientific collaborations to share resources on an unprecedented scale, and for geographically distributed groups to work together in ways that were previously impossible. Similar technologies are being adopted within industry, where they serve as important building blocks for emerging service provider infrastructures. Even though the advantages of this technology for classes of applications have been acknowledged, research in a variety of disciplines, including not only multiple domains of computer science (networking, middleware, programming, algorithms) but also application disciplines themselves, as well as such areas as sociology and economics, is needed to broaden the applicability and scope of the current body of knowledge.