# Signal Preprocessing for Enhanced IoT Device Identification Using Support Vector Machine
## 1 Introduction
This statistical method achieved 99.5% accuracy on a dataset of 2400 signals from 16 devices, which were sampled at 5 Gsps [22]. Signal preprocessing steps included bandpass filtering and amplitude scaling to standardize the signals. Despite its high effectiveness in controlled environments, this approach relies on steady-state noise and remains susceptible to misclassification in noisy, real-world conditions. Short-time Fourier transform (STFT) and carrier frequency offset (CFO) compensation have been used for signal preprocessing LoRa signals, achieving 93.85% accuracy for legitimate device classification and 90.23% for rogue device detection using a KNN classifier [23]. However, this method showed limitations in scalability and robustness, with performance degrading under open-set scenarios with high noise levels and increased dataset openness. In contrast, a federated learning (FL) framework has been proposed to construct RFF datasets for IoT, UAVs, and Bluetooth devices. This framework uses logistic regression to improve generalization under privacy constraints. Although FL addresses data heterogeneity and privacy challenges, it introduces complexity in synchronization and is highly dependent on data quality and distribution [24]. Building on the growing body of research highlighting the critical role of signal preprocessing in RFF analysis, this paper evaluates the impact of a signal preprocessing technique on the effectiveness of an SVM-based classifier used to discriminate Bluetooth devices based on their RF fingerprints. This study demonstrates that an RF signal preprocessing technique applied with four approaches implies a low processing time, but it improves the effectiveness of an SVM-based classifier. Specifically, four variants of a scaling signal preprocessing technique are implemented to address problems such as noise and signal variability, which often negatively affect the performance of classification systems when applied in real-world situations. These signal preprocessing variants are normalized raw signal, mean-normalized raw signal, max-normalized raw signal, and min-normalized raw signal. Therefore, this study presents a systematic analysis to know how different variants of the same parameterized technique of RF signal preprocessing can affect the effectiveness of a classification algorithm used to discriminate Bluetooth devices. This approach demonstrates that the intermediate step between acquiring the RF signals and either defining the RF fingerprints of Bluetooth devices or estimating the radio-in-signal traces is critical to the effectiveness of an identification or discrimination strategy. For the purpose of this study, a Bluetooth signal dataset was used, which contains 150 RF signals from each of 16 smartphones, representing eight different models with two smartphones by model. This Bluetooth signal dataset was constructed from the RF signal dataset created by [PERSON] et al. (2015). In addition, the SVM-based classifier was used considering that it is widely used in fingerprinting research ([PERSON] et al., 2015). The paper is divided into five sections. Section 2 provides a comprehensive overview of the RF signal dataset consisting of Bluetooth signals from multiple devices. It provides the definition and characteristics of these signals and details the RF signal preprocessing possibilities derived from a scaling technique. In addition, this section describes the configuration of the SVM-based classifier used to distinguish Bluetooth devices, including the selection of the kernel, and the experimental setup for training and testing. Section 3 presents the effect of each RF signal preprocessing option on the effectiveness of the SVM-based classifier, using confusion matrices and accuracy metrics in the evaluation. This section shows how different RF signal preprocessing options affect the effectiveness of the classifier considering various device classes.