# Signal Preprocessing for Enhanced IoT Device Identification Using Support Vector Machine
## 4 Discussion

Table 8: Comparison of SVM and Random Forest classifiers on raw and max-normalized signalsAn important observation is the markedly higher classification accuracy achieved for device classes 7, 8, 15, and 16 compared to the other classes. Several factors may contribute to this discrepancy. First, newer generations of Bluetooth hardware often incorporate enhanced transmission circuitry and more stable signal chains, which can yield RF emissions that are both more consistent and more readily distinguishable by an SVM-based classifier. Second, variations in antenna design and architecture influence the antenna gain and radiation pattern; devices with a more uniform gain profile introduce less noise during acquisition, thereby strengthening the intrinsic RF fingerprint. Third, even small fluctuations in data-acquisition conditions--such as temperature, orientation, or slight changes in receiver calibration--can introduce significant variability into the captured RF traces. Notably, as shown in Table 2, classes 7, 8, 15, and 16 were correctly identified even without any of the RF-preprocessing techniques evaluated in this study. Together, these findings emphasize that the ultimate performance of the SVM classifier hinges not only on the preprocessing pipeline used to extract RF fingerprints but also on the underlying hardware generation, antenna characteristics, and experimental acquisition conditions. Table 9 shows a comparison of different signal preprocessing techniques and classifiers used in different studies for classification system based on RF fingerprints. In the proposed study, RF signal preprocessing with MNRS combined with an SVM-based classifier achieves a _diagnostic accuracy_ of 86.21% and an execution time of 15.91 seconds. While this _accuracy_ is lower than several of the referenced studies, which is consistent with the results of [PERSON] et al. (2019) (99.2%) and [PERSON] et al. (2019) (99.5%). On the other hand, the time complexity in the proposed approach is relatively higher compared to state-of-the-art results, where execution times such as 0.21 seconds in ([PERSON] et al., 2019) indicate better computational efficiency. This difference highlights the need to optimize the computational workflow of the proposed approach, especially for real-time IoT environments where rapid authentication is critical. In addition, the signal preprocessing techniques used in this study differ from approaches such as the research work presented by [PERSON] et al. based on CFO (2018), which achieved 97.3% accuracy using CNN on LTE data. Studies such as the work presented by [PERSON] et al. (2019) also show high accuracy (98.84%) using hybrid preprocessing on 4G/5G \begin{table}
\begin{tabular}{l l l l l l} \hline \hline
**Ref.** & **Preprocessing** & **Classifier** & **RF** & **Accuracy** & **Time** \\  & **Variant** & & **Technology** & **[\%]** & **[s]** \\ \hline
[22] & MNRS & JSD & BT & 99.20 & 0.21 \\
[21] & NR & DL-Based & Simulated Signals & 98.00 & NR \\
[15] & CFO & Hybrid & 4/5G devices & 98.84 & NR \\
[14] & CFO & CNN & LTE data & 97.30 & 40.16 \\
[14] & Homomorphic & SHAe & V2X devices & 97.30 & NR \\
[23] & CFO & KNN & LoRa & 93.23 & NR \\
[16] & Normalization & DL-Based & NR & NR & NR \\
[16] & NR & DL Models & RF devices & NR & NR \\
[18] & Data Augmentation & DL-Based & ZigBee devices & 92.68 & NR \\
[17] & NR & RRE & UAV & 95.60 & NR \\
[20] & CFO & CNN & ZigBee devices & 94.81 & NR \\
[28] & Large Scale & FWSResNet & LTE data & 99.50 & NR \\
[29] & CFO & DL-Based & LoRa devices & 96.40 & NR \\
[24] & Large Scale & FL & RF devices & NR & NR \\
[19] & NR & CNN & NR & 99.00 & NR \\ Prop. & MNRS & SVM & BT & 86.21 & 15.91 \\ \hline \hline \end{tabular}
\end{table} 
Table 9: Studies on preprocessing and classification techniques for classifiers based on RF fingerprints. devices, emphasizing the importance of advanced signal preprocessing to improve classification performance.