# Signal Preprocessing for Enhanced IoT Device Identification Using Support Vector Machine
## 2 Materials and Methods
### Definition and Characteristics of Raw Bluetooth Signals  
**Signal Segmentation and Conditioning for the Uzundurukan Dataset**. Each Bluetooth signal carries intrinsic features that form the basis of RFF, which allows devices to be identified based on their unique characteristics, using one or more of their three distinct states. Figure 1a shows how the complete Bluetooth signal as detected by the receiver, while Figure 1b highlights the envelope of the signal, making it easier to observe the transitions between these states. It should be emphasized that the signal envelope was calculated using the simple application of the envelope function of MATLAB(tm) R2023b by MathWorks, Inc., in Massachusetts, USA. The first state, known as the initial state, captures only the internal noise generated by the Bluetooth receiver. At this stage, the transmitter has not yet started sending signals and the noise from the receiver serves as a baseline for further analysis. Once the transmitter is activated, the signal enters the transient state. During this phase, the receiver detects the intrinsic noise of the transmitter in addition to its own noise. This state reflects the transition as the Bluetooth transmitter ramps up, providing valuable insight into the device's unique signal behavior. Finally, the signal reaches the steady state, where both the intrinsic noise of the transmitter and the receiver are combined. This phase represents the full interaction between the two devices and captures the overall noise profile of the communication system. **Signal Segmentation and Conditioning for the Rusins Dataset**. While the Uzundurukan dataset provides pre-segmented RF bursts of fixed duration, the Rusins dataset consists of continuous recordings for each wearable device. To homogenize preprocessing, each complete recording file is segmented into 5 uses slices. This procedure allows separate  
Figure 1: Identification of signal states in Bluetooth RF signals: (**a**) raw Bluetooth signals acquired from smartphone devices, illustrating the three distinct signal states: initial state, transient state, and steady state; (**b**) signal envelopes derived from (**a**), providing a clearer visualization of the transitions between these states. analysis of different intervals within Bluetooth communication and enables evaluation of intra-device variability. Figure 2a shows an example of a full recording (typically 10-30 s) directly from the USRP X310 file. Figure 2b displays one of the 5 \(\upmu\)seg segments extracted from that recording. The 5 \(\upmu\)seg window length balances the need to capture sufficient information from the full signal cycle while enabling processing of multiple segments to increase fingerprinting robustness. After segmentation, each slice is subjected to the bandpass filtering (2.40-2.48 GHz) described in the next section. This ensures that both the 5 \(\upmu\)seg segments and the complete bursts share identical spectral conditioning before feature extraction and RFF classification. On the other hand, Figure 3 illustrates the frequency spectrum of a Bluetooth signal, highlighting the presence of unwanted frequencies combined with noise, which includes environmental electromagnetic interference and system noise. This feature highlights the importance of implementing a bandpass filter to improve signal fidelity. The bandpass filter extracts the signal in the range from 2.40 GHz to 2.48 GHz. The shaded gray area in Figure 3 highlights the effect of the bandpass filter. This process ensures that the raw signal provided to subsequent signal preprocessing methods is cleaner, allowing for more accurate analysis and classification in RFF tasks. Therefore, the raw Bluetooth signal represents the unprocessed transmission captured directly from the target device. This signal contains intrinsic features derived from the device hardware, specifically of the transmitter.