Dataset of WiFi-based Environment-independent In-baggage Object Identification System

<strong>Description:</strong> The dataset of environment-independent in-baggage object identification system leveraging low-cost WiFi. The dataset contains the extracted CSI features from 14 representative in-baggage objects of 4 different materials. The experiments are conducted in 3 different office environments with different sizes. We hope this dataset will help researchers to reproduce the former work of in-baggage object identification through WiFi sensing. <strong>Dataset Format: </strong> .mat files <strong>Section 1: Device Configuration: </strong> <strong>Transmitter: </strong>Aaronia HyperLOG 7060 direction antenna with a Dell Inspiron 3910 desktop for control. <strong>Receiver: </strong>Hawking HD9DP orthogonal antennas with a Dell Inspiron 3910 desktop for control <strong>NIC:</strong> Atheros QCA9590. The configuration and installation guide of CSI tool can be found at https://wands.sg/research/wifi/AtherosCSI/ <strong>WiFi Packet Rate: </strong>1000 pkts/s <strong>Section 2: Data Format</strong> We provide the CSI features through .mat files. The details are shown in the following: 14 different objects made of 4 different materials are included in 3 different environments and 3 different days. Each object is tested for 60 seconds and repeated for 3 times. The dataset file name is presented as "Object_Number". The detailed information are: Object: The object we involved in the experiment (e.g., book, laptop) Number: The number of repeats. <strong>Section 3: Experimental Setups</strong> There are 3 different office experiment setups for our data collection. The detailed setups are shown in the paper. For the objects, we involve 14 types of objects made of 4 different materials. <strong>Environments: </strong> 3 different environments are involved, including 3 office environments with the size of 15 ft × 13 ft, 16 ft × 12 ft, 28 ft × 23 ft, respectively. For each room environment, data is collected on different days and with different furniture settings (i.e., 2 desks and 2 chairs are moved at least 3 ft. ) <strong>Representative objects: </strong> Data is collected using 14 representative objects of 4 different materials including fiber: book, magazine, newspaper; metal: thermal cup, laptop; cotton/polyester: cotton T-shirts (×2), cotton T-shirts (×4), hoodie, polyester T-shirts, polyester pants; water: 1L bottle with 1L water, 1L bottle with 500ml water, 500ml bottle with 500ml water. <strong>Section 4: Data Description</strong> For our data organization, we separate the data files into different folders based on different days and different environments. Under these folders, data are further distributed in terms of different objects and repeat times. All the files are .mat files, which can be directly read for further applications. <strong>Features of CSI amplitude: </strong>We calculate 7 different types of statistical features, including mean, variance, median, skewness, kurtosis, interquartile range and range, and polarization feature from CSI amplitude. Particularly, we calculate the features for all 56 subcarriers with different operating frequencies and responses to the target object. <strong>Features of CSI phase: </strong>For the features of CSI phase, the same features with CSI amplitude are extracted and stored in the dataset. <strong>Section 6: Citations</strong> If your work is related to our work, please cite our papers as follows. https://ieeexplore.ieee.org/document/9637801 Shi, Cong, Tianming Zhao, Yucheng Xie, Tianfang Zhang, Yan Wang, Xiaonan Guo, and Yingying Chen. "Environment-independent in-baggage object identification using wifi signals." In 2021 IEEE 18th International Conference on Mobile Ad Hoc and Smart Systems (MASS), pp. 71-79. IEEE, 2021.描述：本数据集面向基于低成本WiFi实现的与环境无关的行李内物体识别系统。数据集包含4种不同材质的14类典型行李内物体的信道状态信息（Channel State Information，CSI）提取特征。实验在3种不同尺寸的办公环境中开展，旨在助力研究人员复现基于WiFi感知的行李内物体识别相关前期研究工作。 数据集格式：.mat格式文件 第1部分：设备配置 发射端：搭载Aaronia HyperLOG 7060定向天线，配套Dell Inspiron 3910台式机用于控制。 接收端：搭载Hawking HD9DP正交天线，配套Dell Inspiron 3910台式机用于控制。 网络接口卡（Network Interface Card，NIC）：Atheros QCA9590。CSI工具的配置与安装指南可参考https://wands.sg/research/wifi/AtherosCSI/ WiFi数据包速率：1000 数据包/秒 第2部分：数据格式 我们通过.mat格式文件提供CSI特征，详情如下：数据集涵盖3种不同环境、3个不同日期下的4种材质的14类物体。每个物体的测试时长为60秒，且重复测试3次。数据集文件命名格式为"Object_Number"，详细说明如下： Object：实验中涉及的物体（例如书籍、笔记本电脑） Number：重复测试的次数。 第3部分：实验设置 本次数据采集共设置3种不同的办公实验场景，详细场景说明可参见论文。实验涉及的物体为4种材质的14类物体。 实验环境：共包含3种办公环境，尺寸分别为15 ft × 13 ft、16 ft × 12 ft、28 ft × 23 ft。针对每个房间环境，我们在不同日期且调整家具布局（即至少移动2张桌子和2把椅子至3英尺以外）的条件下采集数据。 典型被测物体：本次采集的数据涉及4种材质的14类典型物体，具体包括： 纤维类：书籍、杂志、报纸； 金属类：保温杯、笔记本电脑； 棉/聚酯纤维类：纯棉T恤（×2）、纯棉T恤（×4）、连帽衫、聚酯纤维T恤、聚酯纤维长裤； 水类：1L装盛1L水的水瓶、1L装盛500ml水的水瓶、500ml装盛500ml水的水瓶。 第4部分：数据组织与说明 我们按照不同日期与不同环境将数据文件划分至不同文件夹中，在各文件夹下，进一步按照不同物体与重复次数进行分类存储。所有文件均为.mat格式文件，可直接读取并用于后续研究应用。 CSI幅度特征：我们从CSI幅度中提取了7种不同类型的统计特征与极化特征，包括均值、方差、中位数、偏度、峰度、四分位距与极差。特别地，我们针对所有56个子载波（对应不同工作频率与目标物体的响应）计算了上述特征。 CSI相位特征：针对CSI相位特征，我们提取了与CSI幅度完全一致的特征并存储至数据集中。 引用说明：若您的研究工作与本数据集相关，请按如下方式引用我们的论文： https://ieeexplore.ieee.org/document/9637801 Shi, Cong, Tianming Zhao, Yucheng Xie, Tianfang Zhang, Yan Wang, Xiaonan Guo, and Yingying Chen. "Environment-independent in-baggage object identification using wifi signals." In 2021 IEEE 18th International Conference on Mobile Ad Hoc and Smart Systems (MASS), pp. 71-79. IEEE, 2021.