A novel distance vector hop localization method for wireless sensor networks

IF 2.1 Q3 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE Journal of Intelligent Systems Pub Date : 2023-01-01 DOI:10.1515/jisys-2023-0031
Y. A. A. S. Aldeen, S. Kadhim, N. N. Kadhim, Syed Hamid Hussain Madni
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引用次数: 1

Abstract

Abstract Wireless sensor networks (WSNs) require accurate localization of sensor nodes for various applications. In this article, we propose the distance vector hop localization method (DVHLM) to address the node dislocation issue in real-time networks. The proposed method combines trilateration and Particle Swarm Optimization techniques to estimate the location of unknown or dislocated nodes. Our methodology includes four steps: coordinate calculation, distance calculation, unknown node position estimation, and estimation correction. To evaluate the proposed method, we conducted simulation experiments and compared its performance with state-of-the-art methods in terms of localization accuracy with known nodes, dislocated nodes, and shadowing effects. Our results demonstrate that DVHLM outperforms the existing methods and achieves better localization accuracy with reduced error. This article provides a valuable contribution to the field of WSNs by proposing a new method with a detailed methodology and superior performance.
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一种新的无线传感器网络距离矢量跳定位方法
摘要无线传感器网络(WSNs)需要精确定位传感器节点以满足各种应用需求。在本文中,我们提出了距离矢量跳定位方法(DVHLM)来解决实时网络中的节点错位问题。该方法结合了三边测量和粒子群优化技术来估计未知或错位节点的位置。该方法包括坐标计算、距离计算、未知节点位置估计和估计校正四个步骤。为了评估所提出的方法,我们进行了仿真实验,并将其在已知节点、错位节点和阴影效果的定位精度方面与最先进的方法进行了比较。结果表明,该方法优于现有的定位方法,在误差较小的情况下获得了更好的定位精度。本文提出了一种方法详细、性能优越的无线传感器网络新方法,为无线传感器网络领域做出了宝贵的贡献。
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来源期刊
Journal of Intelligent Systems
Journal of Intelligent Systems COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE-
CiteScore
5.90
自引率
3.30%
发文量
77
审稿时长
51 weeks
期刊介绍: The Journal of Intelligent Systems aims to provide research and review papers, as well as Brief Communications at an interdisciplinary level, with the field of intelligent systems providing the focal point. This field includes areas like artificial intelligence, models and computational theories of human cognition, perception and motivation; brain models, artificial neural nets and neural computing. It covers contributions from the social, human and computer sciences to the analysis and application of information technology.
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