RSSI Measurements of Beacon Frames from Wi-Fi Radio Waves

We collected data to train the ML module to determine the user’s device's location based on beacon frame characteristics and RSSI values from Wi-Fi APs. To collect the data, we defined a threshold distance of 7 feet as the maximum allowable distance between the user’s devices. We then collected two datasets: one with data collected while the two Raspberry Pis were within 7 feet or less of each other named ”authentic”, and another with data collected while the distance between the two Raspberry Pis was over 7 feet named ”unauthorized”. While collecting the datasets, we have repeatedly placed the Pis farther/closer to diversify the data samples with respect to the dataset type i.e., for the ”authentic” the distance between Pis kept below 7 feet; whereas for the ”unauthorized” dataset the Pis are placed a minimum of 7.5 feet away. This helped to determine the ”gray area” between the acceptable threshold distance and the distance at which access should be denied. We collected a total of 4,825 samples of data from two Raspberry Pis for the two datasets (with 2442 samples in the ”authentic” dataset and 2383 samples in the ”unauthorized” dataset). These samples were collected using 10 different Wi-Fi Aps at various locations where are these locations? and times. The resulting datasets consisted of six columns: ”RPi”, ”SSID,”, ”Frequency (Hz)”, ”RSSI (dBm)”, ”Location”, and ”Label.” The ”RPi” column specifies which Raspberry Pi collected the data, while the ”SSID” column listed the name of the Wi-Fi AP. The ”Frequency (Hz)” column determines the frequency of the Wi-Fi AP in Hz, and the ”RSSI (dBm)” column shows the RSSI value in dBm. The ”Location” column indicates where the data was collected, and the ”Label” column is a categorical column (0 or 1) determining whether the sample is authentic or not. A value of 1 indicates that the sample is ”authentic”, while a value of 0 indicates that the sample is ”unauthorized”.

本团队搜集数据以训练机器学习模块，旨在根据信标帧特征和来自Wi-Fi接入点的信号强度指示（RSSI）值确定用户设备的地理位置。为收集数据，我们设定了7英尺作为用户设备之间最大可接受距离的阈值。随后，我们收集了两个数据集：一个是在两台树莓派相距7英尺或更近时收集的数据，命名为“真实数据集”，另一个是在两台树莓派相距超过7英尺时收集的数据，命名为“未经授权数据集”。在收集数据集的过程中，我们反复调整树莓派之间的距离，以丰富数据样本的多样性，具体而言，对于“真实数据集”，树莓派之间的距离保持在7英尺以下；而对于“未经授权数据集”，树莓派被放置在至少7.5英尺的间隔。这一做法有助于确定可接受阈值距离与应拒绝访问的距离之间的“灰色地带”。我们共从两台树莓派中收集了4,825个数据样本，用于两个数据集（其中“真实数据集”包含2,442个样本，“未经授权数据集”包含2,383个样本）。这些样本是在10个不同的Wi-Fi接入点及其不同位置和时段收集的。生成的数据集包含六个列：”RPi”（树莓派标识）、”SSID”（接入点名称）、”频率（Hz）”、“RSSI（dBm）”、“位置”和”标签”。”RPi”列指定了收集数据的树莓派型号，”SSID”列列出了Wi-Fi接入点的名称，”频率（Hz）”列确定了接入点的频率（单位为Hz），”RSSI（dBm）”列显示了RSSI值（单位为dBm）。”位置”列指示了数据的收集地点，”标签”列是一个分类列（0或1），用于确定样本是否为“真实”的。其中，1表示样本为“真实”，而0表示样本为“未经授权”。