# Signal Preprocessing for Enhanced IoT Device Identification Using Support Vector Machine
## 2 Materials and Methods  
### Dataset Description  
The RF signal dataset used in this work was extracted from the RF signal dataset created by [PERSON] et al. (2015). They captured RF signals from 27 smartphones, representing six different manufacturers. For each Bluetooth, they captured 150 RF signals, for a total of 4050 signal recordings. They acquired RF signals under controlled conditions to minimize external interference and to ensure high data quality and consistency. The [PERSON] dataset includes RF signals recorded at 5 Gsps, 10 Gsps, and 20 Gsps using a TDS7404 oscilloscope manufactured by Tektronix, Inc., located in Oregon, USA. Inaddition, lower-frequency signals were acquired at 250 Msps using a modular RF front-end system connected to the oscilloscope. To complement the experimental evaluation, we also considered the publicly available dataset by [PERSON] et al. (2018), which focuses on Bluetooth and Bluetooth Low Energy (BLE) communications from wearable devices. This dataset captures the complete communication cycle--including advertising, pairing, data exchange, and disconnection--under highly controlled conditions within an anechoic chamber to ensure RF isolation. A total of 32 wearable Bluetooth devices were used, including smartwatches, fitness bands, and headsets, with each device recorded in both paired and unpaired modes. The recordings were conducted using a USRP X310 software-defined radio (SDR) with a CBX-120 daughterboard, at a sample rate of 100 MHz and saved as complex64 I/Q samples. Each recording lasts approximately 10 to 30 s and includes metadata with precise timing of Bluetooth events. The dataset also includes baseline noise recordings and was specifically designed to support RF fingerprinting, Bluetooth protocol analysis, and cybersecurity research. For this work, a total of 24 device-class radios were selected: 16 smartphones from the Uzundurukan dataset and 8 additional wearable Bluetooth devices from the Rusins dataset. The 16 smartphones were organized into 8 twin pairs, each representing a different brand or model. Each device in a twin pair was treated as a distinct class, even though both belonged to the same model. For example, as shown in Table 1, Class 1 corresponds to \"iPhone 5 - 1\", while Class 2 corresponds to its twin, \"iPhone 5 - 2\". This approach of categorization was consistently applied to all other smartphone pairs. Similarly, the four wearable devices from the Rusins dataset--each belonging to a different brand and device type--were also assigned to separate classes, resulting in a total of 24 unique classes across both datasets.  
Understanding the distinctions between these classes is critical for evaluating RFF performance, as subtle variations between twin devices test the robustness of the classification algorithm. These class distinctions are further explored in the next section, where the definition and characteristics of raw Bluetooth signals are discussed in detail. This provides  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Class** & **Device Name** \\ \hline
1 & Amazfit Band 5 \\
2 & Apple Watch SE \\
3 & Fitbit Charge 5 \\
4 & Fitbit Versa 4 \\
5 & Garmin Insinct Crossover \\
6 & Garmin Insinct SQ \\
7 & Apple Watch Series 8 \\
8 & iPhone 5 - 1 \\
9 & iPhone 5 - 2 \\
10 & iPhone 6 - 1 \\
11 & iPhone 6 - 2 \\
12 & iPhone 5s - 1 \\
13 & iPhone 5s - 2 \\
14 & iPhone 6s - 1 \\
15 & iPhone 6s - 2 \\
16 & LG G4 - 1 \\
17 & LG G4 - 2 \\
18 & Samsung Note3 - 1 \\
19 & Samsung Note3 - 2 \\
20 & Samsung S5 - 1 \\
21 & Samsung S5 - 2 \\
22 & Samsung Galaxy S20 FE \\
23 & Sony Xperia M5 - 1 \\
24 & Sony Xperia M5 - 2 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Smartphone classes and models in the case study.  
the foundation for analyzing how these signals can be used to uniquely identify devices within and across classes.