利用机器学习算法改进物联网系统中的RSSI室内定位

Signals Pub Date : 2023-09-25 DOI:10.3390/signals4040036
Madduma Wellalage Pasan Maduranga, Valmik Tilwari, Ruvan Abeysekera
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引用次数: 0

摘要

机器学习算法的最新发展在无线通信和物联网(IoT)系统中发挥着重要作用。基于位置的物联网服务(LBIoTS)被认为是这些物联网应用中的主要应用之一。LBIoTS中涉及的关键信息是找到对象的地理位置。全球定位系统(GPS)技术在室内环境中由于多路径的存在而表现不佳。对于室内定位场景,已经研究了许多方法。然而,在这种应用中,由于高信号波动,对运动物体的精确位置估计是具有挑战性的。因此,本文提出了基于蓝牙低功耗(BLE)节点收集的接收信号强度指标(RSSI)值来估计物体位置的机器学习算法。在这个实验中,我们使用了一个公开可用的RSSI数据集。RSSI数据来自不同的BLE ibeacon节点,这些节点安装在复杂的室内环境中,并带有标签。然后,采用加权最小二乘法对RSSI数据进行线性化处理,并采用移动平均滤波器进行滤波。此外,机器学习算法用于训练和测试数据集,以估计物体的精确位置。在不同的超参数下对所提出的算法进行了测试和评价。经过测试的模型对KNN的准确率约为85%,对SVM的准确率为84%,对FFNN的准确率为76%。
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Improved RSSI Indoor Localization in IoT Systems with Machine Learning Algorithms
Recent developments in machine learning algorithms are playing a significant role in wireless communication and Internet of Things (IoT) systems. Location-based Internet of Things services (LBIoTS) are considered one of the primary applications among those IoT applications. The key information involved in LBIoTS is finding an object’s geographical location. The Global Positioning System (GPS) technique does not perform better in indoor environments due to multipath. Numerous methods have been investigated for indoor localization scenarios. However, the precise location estimation of a moving object in such an application is challenging due to the high signal fluctuations. Therefore, this paper presents machine learning algorithms to estimate the object’s location based on the Received Signal Strength Indicator (RSSI) values collected through Bluetooth low-energy (BLE)-based nodes. In this experiment, we utilize a publicly available RSSI dataset. The RSSI data are collected from different BLE ibeacon nodes installed in a complex indoor environment with labels. Then, the RSSI data are linearized using the weighted least-squares method and filtered using moving average filters. Moreover, machine learning algorithms are used for training and testing the dataset to estimate the precise location of the objects. All the proposed algorithms were tested and evaluated under their different hyperparameters. The tested models provided approximately 85% accuracy for KNN, 84% for SVM and 76% accuracy in FFNN.
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CiteScore
3.20
自引率
0.00%
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审稿时长
11 weeks
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