# Signal Preprocessing for Enhanced IoT Device Identification Using Support Vector Machine
## 1 Introduction  
Signal preprocessing is critical for reliable data analysis, especially in applications such as healthcare [1, 2], financial forecasting [3, 4], and security [5, 6], where high-quality data or signals are essential for accurate decision-making. This process addresses the inherent complexity of data quality deficiencies by employing techniques such as inconsistency removal and missing value imputation, which can improve the classification accuracy by up to 80% [7]. The global data preparation tools market, valued at USD 4.86 billion in 2022, is projected to grow at a CAGR of 16.1% between 2023 and 2030, reflecting its increasing adoption in industries that rely on data-driven insights [8]. One prominent area where the impact of data preprocessing is particularly evident is in the analysis of radio frequency fingerprints (RFFs). These are unique and inherent signatures produced by radio frequency electronic devices during wireless communication and offer immense potential for applications in wireless security, IoT device authentication, and spectrum monitoring [9, 10]. Ensuring the reliability of RFF data requires robust signalpreprocessing techniques to mitigate challenges such as noise, acquisition inconsistencies, and information loss, which can otherwise compromise the effectiveness of identification and classification systems [11; 12]. For this purpose, five data preprocessing paradigms have been identified: data cleaning, data reduction, data transformation, data partitioning, and data scaling [13]. These paradigms are used to improve the quality of raw data, ensuring accurate and efficient analytical results in RFF analysis. Recent advances in data preprocessing and classification techniques have significantly improved the accuracy of various machine learning applications but also revealed notable challenges. For instance, task-oriented filtering and homomorphic processing combined with SHAP-based classifiers achieved 97.3% accuracy for vehicle-to-everything (V2X) devices. However, these methods have limited generalization [14]. Similarly, Wavelet Transform and differential spectrum analysis using autoencoder-based schemes achieved 98.84% accuracy for LTE device categories, but computational complexity and noise sensitivity restrict scalability [15]. Deep learning models for the RFF identification demonstrate over 90% accuracy under controlled conditions but are susceptible to signal variability in real-world deployments [16]. In energy analysis, semi-supervised learning and data augmentation techniques improve reliability but are constrained by the need for large, labeled datasets and consistent signal preprocessing [13]. Random railings enhancement (RRE) improves minority class representation in unbalanced datasets, but its performance is sensitive to the underrepresentation of certain classes [17]. Supervised contrastive learning-based CNNs (CNN-SCL) achieve 92.68% accuracy with limited samples, although the results depend heavily on the quality of feature encoding [18]. Neural synchronization, which combines signal processing with data-driven learning, improves open-set discrimination. However, its success depends on the quality of the signal preprocessing for generalization [19]. The application of Gramian angular fields (1D/2D) paired with channel-selectable CNNs achieves up to 99.26% accuracy at high SNR, but its effectiveness requires clean signal segments [20]. Similarly, channel-independent spectrograms coupled with deep metric learning offer scalability and robust RFF identification for LoRa devices, although computational overhead remains a limitation for real-time applications. Virtual database generators, such as RiPyFi-VDG, simulate hardware impairments to achieve an average of 94% accuracy; however, they may introduce biases that hinder generalization to real-world scenarios [21]. In another approach, the probability density function (PDF) of noise signals from Bluetooth RF signals was compared using Jensen-Shannon divergence (JSD) for device identification.