# Signal Preprocessing for Enhanced IoT Device Identification Using Support Vector Machine
## 5 Conclusion and Future Work  
This study demonstrates the critical role of RF signal preprocessing techniques in improving the performance of an SVM-based classifier for Bluetooth device identification via radio frequency fingerprints. Our experiments on two publicly available datasets, one containing smartphone signals and another capturing wearable device signals, show that preprocessing effectively mitigates noise and acquisition inconsistencies in raw RF traces, yielding substantial gains in classification accuracy. The integration of RF recordings acquired in 2020 (smartphone bursts) and 2024 (wearable device streams) within a unified evaluation framework demonstrates that the performance gains afforded by extreme-value normalization remain consistent despite temporal shifts and hardware heterogeneity. This cross-dataset validation underscores the universality of peak-based preprocessing for extracting robust RF fingerprints across diverse Bluetooth platforms. We emphasize that the [PERSON] et al. (2024) dataset includes RF signals from real-world scenarios because the signals were captured from continuous Bluetooth streams under actual operating conditions. Wehave successfully applied our preprocessing techniques to these live recordings, which contain several RF signals within the same bandwidth. These RF signals were then filtered, preprocessed, and classified. Among the preprocessing variants tested, max-normalized raw signals delivered the best results: a _diagnostical accuracy_ of 86.21% and perfect _effective accuracy_ of 100.00% with the SVM classifier. Mean and minimum normalization also provided strong improvements (diagnostical precision above 82% and effective accuracies of 95%), and simple normalization increased diagnostic precision from 30. 59% to 51.06% on raw signals. These gains underscore the importance of scaling signal peaks to capture the most discriminative RF features. To verify that this benefit is not specific to SVM, we compared its performance with a Random Forest classifier. Although Random Forest achieved an effective accuracy of 39.57% on raw signals, max-normalized preprocessing boosted it to 78.09%, confirming that preprocessing enhancements generalize across model architectures. Despite these advances, challenges remain. Misclassifications still occur between devices with highly similar RF characteristics, and computational overhead for real-time deployment can be significant. Moreover, the generalization of this method to different acquisition setups and environmental noise has not been fully explored. Future work should pursue more sophisticated feature extraction, such as time-frequency transforms or deep learning representations to further increase separability. The ensemble or hybrid classifiers may take advantage of complementary strengths of multiple algorithms. It will also be critical to validate preprocessing methods under realistic conditions: introducing ambient interference, varying hardware configurations, and scaling to larger, more diverse device populations. Finally, extending the analysis to additional classifiers (e.g., neural networks, k-nearest neighbors) will help confirm the universality of preprocessing benefits while maintaining focus on the preprocessing strategies themselves. By addressing these areas, we can advance towards robust, scalable RF fingerprinting systems that secure a broad range of IoT and wearable devices in real-world deployments. Conceptualization, M.M. and R.V.-M.; Methodology, R.F.S.-C., M.M. and R.V.-M.; Software, R.F.S.-C.; Validation, M.M., D.A.-T. and R.V.-M.; Formal analysis, M.M. and R.V.-M.; Investigation, R.F.S.-C., M.M. and R.V.-M.; Data curation, R.F.S.-C., D.A.-T. and R.A.V.-D.; Writing--original draft, R.F.S.-C. and R.V.-M.; Writing--review and editing, M.M., R.A.V.-D. and R.V.-M.; Visualization, M.M., R.A.V.-D. and D.A.-T.; Supervision, M.M. and R.V.-M.; Project administration, R.V.-M.; Funding acquisition, R.V.-M. All authors have read and agreed to the published version of the manuscript.