# Signal Preprocessing for Enhanced IoT Device Identification Using Support Vector Machine
## 2 Materials and Methods
### RF Signal Preprocessing Technique  
RF signal preprocessing standardizes the input data to ensure consistent signal characteristics that facilitate interpretation by the classifier. In this study, a general scaling method is used to adjust the amplitude of raw Bluetooth signals. This approach improves classification accuracy by emphasizing key signal features while preserving the intrinsic characteristics of the data. Each signal is represented as \(s_{i}(t)\), where \(i=1,2,3,\ldots N\), and its amplitude is adjusted using a parameterized scaling function. The objective is to improve both the consistency and the accuracy of the classification process by selecting an appropriate scaling factor.  
**Definition 1** (Parameterized signal scaling).: _The signal \(p_{i}(t)\), given in Equation (1), is the scaled variant of the Bluetooth signal \(s_{i}(t)\), assuming that \(\alpha/A_{i}^{peak}\) is the scaling factor, \(A_{i}^{peak}=max|s_{i}(t)|\) represents the maximum magnitude reached by \(s_{i}(t)\), and \(\alpha\) is a dynamic term for adjusting the scaling factor that depends on the selected signal preprocessing method._  
\[p_{i}(t)=\frac{\alpha}{A_{i}^{peak}}\cdot s_{i}(t)\, \tag{1}\]  
_where \(i=1,2,3,\ldots N\)._  
Definition 1 ensures that the raw Bluetooth signals are dynamically adjusted based on \(A_{i}^{peak}\) and \(\alpha\), preserving their time-varying features and avoiding the significant differences in amplitude of a variety of Bluetooth signals produced by the same device.  
**Definition 2** (Normalized raw signal).: \(p_{i}(t)\) _is the normalized variant of \(s_{i}(t)\) when \(\alpha=1\), meaning that \(s_{i}(t)\) is scaled by its maximum value, without any other scaling adjustment._  
Figure 3: Frequency spectrum of Bluetooth signal Before and after bandpass filtering.  
The envelopes of the normalized variants computed for the Bluetooth signals in Figure 4 are shown in Figure 5.  
**Definition 3** (Mean-normalized raw signal).: \(p_{i}(t)\) _is the mean-normalized variant of \(s_{i}(t)\) when \(\alpha\) is computed using Equation (2) and \(N\) Bluetooth signals from the same Bluetooth device are considered._  
\[\alpha=\frac{1}{N}\sum_{i=1}^{N}A_{i}^{peak}. \tag{2}\]  
It should be noted that this approach reduces signal variability by applying a global scaling factor. Figure 6 illustrates the envelopes of the mean-normalized raw signals for the three Bluetooth signals of Figure 4. Typically, \(N=150\).  
Figure 4: Envelops of the three Bluetooth signals of steady state acquired from iPhone 5-1.  
Figure 5: Envelopes of the normalized raw signal peaks corresponding to Figure 4.  
Figure 6: Envelopes of the mean-normalized raw signals for signals shown in Figure 4.  
**Definition 4** (Max-normalized raw signal).: \(p_{i}(t)\) _is the max-normalized variant of \(s_{i}(t)\) when \(\alpha\) is computed using Equation (3) and \(N\) Bluetooth signals from the same Bluetooth device are considered._  
\[\alpha=\max\left(A_{i}^{peak}\right)\,. \tag{3}\]  
This approach uses the global maximum in the set of Bluetooth signals from the same device. Figure 7 illustrates the envelopes of the max-normalized raw signals for the three Bluetooth signals of Figure 4.  
**Definition 5** (Min-normalized raw signal).: \(p_{i}(t)\) _is the min-normalized variant of \(s_{i}(t)\) when \(\alpha\) is computed using Equation (4) and \(N\) Bluetooth signals from the same Bluetooth device are considered._  
\[\alpha=\min\left(A_{i}^{peak}\right)\,. \tag{4}\]  
This approach minimizes the influence of high peaks in the set of Bluetooth signals from the same device, emphasizing lower signal components. Figure 8 illustrates the envelopes of the min-normalized raw signals for the three Bluetooth signals of Figure 4.