LoRa sensor data sets for RF finger printing via Self-Organizing Feature Maps

This data repository contains data sets for the RF finger printing of LoRa sensors as described in the paper by Manish Nair, Shuping Dang, Tommaso Cappello & Mark Beach, “IoT Device Authentication Using Self-Organizing Feature Map Data Sets” as submitted for review to the the IEEE Communications Magazine. This contribution relates to this call for papers https://www.comsoc.org/publications/magazines/ieee-communications-magazine/cfp/data-sets-machine-learning-wireless .

本数据集仓库收录了用于LoRa传感器（LoRa sensors）射频指纹识别（RF fingerprinting）的数据集，相关内容源自Manish Nair、Shuping Dang、Tommaso Cappello与Mark Beach合著的论文《基于自组织特征映射（Self-Organizing Feature Map）数据集的物联网（IoT）设备认证》，该论文已提交至《IEEE通信杂志（IEEE Communications Magazine）》进行同行评审。本数据集与本次征文邀约相关，对应链接为：https://www.comsoc.org/publications/magazines/ieee-communications-magazine/cfp/data-sets-machine-learning-wireless

搜集汇总

背景与挑战

背景概述

该数据集包含用于LoRa传感器射频指纹识别的数据，旨在通过自组织特征映射方法进行物联网设备认证。数据集分为两部分：原始LoRa I/Q信号数据和经过无监督批处理SOFM竞争学习算法处理后的输出图像，适用于机器学习和安全研究领域。