A Localization Algorithm Joining DV-Hop, LSSVM, and Expected Distance Estimation in IoT of Agriculture in Mountainous and Hilly Areas

IF 4.3 2区综合性期刊 Q1 ENGINEERING, ELECTRICAL & ELECTRONIC

IEEE Sensors Journal Pub Date : 2025-03-24 DOI:10.1109/JSEN.2025.3549416

Rutao Tang;Hongyu Hu;Zongqing Li;Shiyuan Wang;Fuliang He

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引用次数: 0

Abstract

The problem of wireless sensor networks (WSNs) node localization is one of the hottest research topics in the application of internet of things (IoT). DV-Hop has recently significantly pushed the state of the art in node localization algorithms. However, DV-Hop and its variants cannot achieve excellent performance, when estimating distance independently in the application of WSNs deployed in mountainous and hilly areas. Therefore, this article proposes a novel node localization approach that combines DV-Hop, least squares support vector machine (LSSVM), and expected distance estimation in order to effectively calculate the estimated distance of nodes in IoT of agriculture. First, the estimated expected distance is proposed based on the different number of hops from the unknown node to the beacon node. Second, a regression model based on LSSVM is applied to predict the distance between the unknown node and the beacon node. Additionally, three objective functions for joint estimation of localization information are constructed. Finally, multiobjective golden eagle optimization (MOGEO) is utilized to solve the coordinates of the unknown node. The experimental results indicate that the average positioning error (APE) of the proposed approach is promising, and reduces node localization error by 39.6% on average with APE compared to the original distance-vector hop (DV-Hop) and its variants in randomly distributed networks.

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一种结合DV-Hop、LSSVM和期望距离估计的山地丘陵农业物联网定位算法

无线传感器网络（WSNs）节点定位问题是物联网（IoT）应用中的热点研究课题之一。最近，DV-Hop极大地推动了节点定位算法的发展。然而，在山区和丘陵地区部署的WSNs应用中，当独立估计距离时，DV-Hop及其变体无法获得优异的性能。为此，本文提出了一种结合DV-Hop、最小二乘支持向量机（LSSVM）和期望距离估计的节点定位方法，以有效计算农业物联网中节点的估计距离。首先，根据未知节点到信标节点的不同跳数，提出估计的期望距离；其次，采用基于LSSVM的回归模型预测未知节点与信标节点之间的距离；在此基础上，构造了三个联合估计定位信息的目标函数。最后，利用多目标金鹰优化算法求解未知节点的坐标。实验结果表明，该方法具有较好的平均定位误差（APE），在随机分布网络中，与原始距离向量跳（DV-Hop）及其变体相比，平均降低了39.6%的节点定位误差。

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

IEEE Sensors Journal 工程技术-工程：电子与电气

CiteScore

7.70

自引率

14.00%

发文量

2058

审稿时长

5.2 months

期刊介绍： The fields of interest of the IEEE Sensors Journal are the theory, design , fabrication, manufacturing and applications of devices for sensing and transducing physical, chemical and biological phenomena, with emphasis on the electronics and physics aspect of sensors and integrated sensors-actuators. IEEE Sensors Journal deals with the following: -Sensor Phenomenology, Modelling, and Evaluation -Sensor Materials, Processing, and Fabrication -Chemical and Gas Sensors -Microfluidics and Biosensors -Optical Sensors -Physical Sensors: Temperature, Mechanical, Magnetic, and others -Acoustic and Ultrasonic Sensors -Sensor Packaging -Sensor Networks -Sensor Applications -Sensor Systems: Signals, Processing, and Interfaces -Actuators and Sensor Power Systems -Sensor Signal Processing for high precision and stability (amplification, filtering, linearization, modulation/demodulation) and under harsh conditions (EMC, radiation, humidity, temperature); energy consumption/harvesting -Sensor Data Processing (soft computing with sensor data, e.g., pattern recognition, machine learning, evolutionary computation; sensor data fusion, processing of wave e.g., electromagnetic and acoustic; and non-wave, e.g., chemical, gravity, particle, thermal, radiative and non-radiative sensor data, detection, estimation and classification based on sensor data) -Sensors in Industrial Practice