Dataset of Wi-Fi packet captures including RSSI for different distances
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The folder contains a dataset of Wi-Fi packet captures using a passive sniffing technique as described in : M. I. Syed, A. Fladenmuller, M. Dias de Amorim. Unity is strength: Improving Wi-Fi passive measurements through sniffer redundancy. Ad Hoc Networks, 2023, 151, pp.103287. https://doi.org/10.1016/j.adhoc.2023.103287 M. I. Syed. Wireless passive measurements: tool, redundancy, measurements, and analyses. Networking and Internet Architecture [cs.NI]. Sorbonne Université, 2023. English. https://theses.hal.science/tel-04258062 M. I. Syed, A. Fladenmuller, M. Dias de Amorim. PyPal: Wi-Fi Trace Synchronization and Merging Python Tool. [Technical Report] LIP6 UMR 7606, Sorbonne Université, France. 2022. https://hal.science/hal-03618014 Dataset In the experiment, ten sniffers capture packets from a given single source node at multiple distances (1m, 10m, 20m, 30m, 40m, and 50m). Each folder contains the dataset for a specific distance. In each folder, you will find three files: The raw data in `pcap` format. The raw data in `csv` format. Synchronized data in `csv` format after using the PyPal tool (https://hal.science/hal-03618014). In the file name, `Sx` indicates the number of the sniffer (`S01` to `S10`). Each file contains eight columns: | Frame_number | Frame_time_epoch | RSSI_dBm | Channel | Frame_type | Frame_subtype | Retransmission | Source_MAC_address | Sequence_number |` Note that : `Frame_number`: number of the received frame in the trace captured by the sniffer. `Sequence_number`: sequence number of the frame sent by our transmitter (Note that missing sequence numbers refers to frames that haven't been captured by the sniffer). The `Source_MAC_address` is identical in each trace as it refers to the MAC address of our transmitter. Devices The devices in the experiment were composed of a Raspberry Pi 4 node and an external Alfa antenna (AlfaAWUS051NH), both for the source node and the sniffers.
本文件夹包含采用被动嗅探技术获取的Wi-Fi数据包捕获数据集，相关研究文献如下： 1. M. I. Syed, A. Fladenmuller, M. Dias de Amorim. 团结则存：通过嗅探器冗余提升Wi-Fi被动测量性能. Ad Hoc Networks, 2023, 151, pp.103287. https://doi.org/10.1016/j.adhoc.2023.103287 2. M. I. Syed. 无线被动测量：工具、冗余方案、测量与分析. 网络与互联网架构 [cs.NI]. 索邦大学, 2023. 英文原文. https://theses.hal.science/tel-04258062 3. M. I. Syed, A. Fladenmuller, M. Dias de Amorim. PyPal：Wi-Fi轨迹同步与合并Python工具. [技术报告] 法国索邦大学LIP6 UMR 7606实验室, 2022. https://hal.science/hal-03618014 数据集说明 本实验中，10台嗅探器在多个距离（1m、10m、20m、30m、40m及50m）下捕获单个源节点发出的数据包。每个子文件夹对应一个特定距离的数据集。每个子文件夹内包含三类文件： - `pcap` 格式的原始捕获数据 - `csv` 格式的原始捕获数据 - 使用PyPal工具（https://hal.science/hal-03618014）处理后得到的同步化`csv`格式数据。 文件名规则：`Sx` 代表嗅探器编号（`S01` 至 `S10`）。每个数据文件包含8列字段，具体如下： | 帧编号 | 帧时间戳（纪元格式） | 接收信号强度指示（dBm） | 信道编号 | 帧类型 | 帧子类型 | 重传标记 | 源MAC地址 | 序列号 | 字段说明： - **帧编号**：嗅探器捕获的轨迹中接收帧的序号 - **序列号**：发射节点发送的帧的序列号（注：缺失的序列号代表未被该嗅探器捕获的帧） - **源MAC地址**：所有轨迹中的该字段均保持一致，对应本次实验所用发射节点的MAC地址 设备说明： 本次实验所用设备分为源节点与嗅探器两类，均采用树莓派4（Raspberry Pi 4）节点搭配外置Alfa天线（AlfaAWUS051NH）。
