基于智能群优化的物联网无距离定位方法

IF 2.4 Q3 COMPUTER SCIENCE, INFORMATION SYSTEMS
Abdelali Hadir, Naima Kaabouch, Mohammed-Alamine El Houssaini, Jamal El Kafi
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引用次数: 0

摘要

最近,传感器节点的精确位置已经成为物联网(IoT)应用领域的一个重大挑战,包括无线传感器网络(wsn)。在这些应用中,准确确定检测到的事件的地理坐标具有至关重要的意义。尽管DV-Hop因其成本效益、可行性和缺乏额外的硬件需求而受到欢迎,但它仍然受到相对明显的本地化错误的阻碍。为了克服这一局限性,本研究引入了将DV-Hop与鸡群优化(CSO)相结合的三种新的定位方法。主要目的是提高基于dv - hop方法的精度。在本文中,我们比较了所提出的定位算法与其他现有方法的效率,包括几种基于粒子群优化(PSO)的算法,同时考虑了随机网络拓扑结构。仿真结果验证了所提算法的有效性。与现有模型相比,本文提出的HW-DV-HopCSO算法在定位精度上有了较大的提高。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Range-Free Localization Approaches Based on Intelligent Swarm Optimization for Internet of Things
Recently, the precise location of sensor nodes has emerged as a significant challenge in the realm of Internet of Things (IoT) applications, including Wireless Sensor Networks (WSNs). The accurate determination of geographical coordinates for detected events holds pivotal importance in these applications. Despite DV-Hop gaining popularity due to its cost-effectiveness, feasibility, and lack of additional hardware requirements, it remains hindered by a relatively notable localization error. To overcome this limitation, our study introduces three new localization approaches that combine DV-Hop with Chicken Swarm Optimization (CSO). The primary objective is to improve the precision of DV-Hop-based approaches. In this paper, we compare the efficiency of the proposed localization algorithms with other existing approaches, including several algorithms based on Particle Swarm Optimization (PSO), while considering random network topologies. The simulation results validate the efficiency of our proposed algorithms. The proposed HW-DV-HopCSO algorithm achieves a considerable improvement in positioning accuracy compared to those of existing models.
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来源期刊
Information (Switzerland)
Information (Switzerland) Computer Science-Information Systems
CiteScore
6.90
自引率
0.00%
发文量
515
审稿时长
11 weeks
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