Abstract
Classifying fluctuating operating wireless environments can be crucial for successfully delivering authentic and confidential packets and for identifying legitimate signals. This study utilizes raw in-phase (I) and quadrature-phase (Q) samples, exclusively, to develop a low-order statistical feature set for wireless signal classification. Edge devices making decentralized decisions from I/Q sample analysis is beneficial. Implementing appropriate security and transmitting mechanisms, reducing retransmissions and increasing energy efficiency are examples. Wireless sensor networks (WSNs) and their Internet of Things (IoT) utilization emphasize the significance of this time series classification problem. Here, I/Q samples of typical WSN and industrial, scientific and medical band transmissions are collected in a live operating environment. Analog Pluto software-defined radios and Raspberry Pi devices are utilized to achieve a low-cost yet high-performance testbed. Features are extracted from Matlab-based statistical analysis of the I/Q samples across time, frequency (fast Fourier transform) and space (probability density function). Noise, ZigBee, continuous wave jamming, WiFi and Bluetooth signal data are examined. Supervised machine learning approaches, including support vector machines, Random Forest, XGBoost, k nearest neighbors and a deep neural network (DNN), evaluate the developed feature set. The optimal approach is determined as an XGBoost/SVM classifier. This classifier achieves similar accuracy and generalization results, on unseen data, to the DNN, but for a fraction of time and computation requirements. Compared to existing approaches, this study’s principal contribution is the developed low-order feature set that achieves signal classification without prior network knowledge or channel assumptions and is validated in a real-world wireless operating environment. The feature set can extend the development of resource-constrained edge devices as it is widely deployable due to only requiring received I/Q samples and these features are warranted as IoT devices become widely used in various modern applications.
Highlights
Wireless sensor networks (WSNs) are increasingly being integrated into safety-critical applications such as, for example, the Internet of Things (IoT), which has evolved WSNs into essential elements of current technology
Despite the trend to use deep learning approaches, as specified in Section 2, this study proves that potent data analysis and signal processing permit traditional techniques to still be effective (Table 7) when paired with sufficiently descriptive feature sets based on time, frequency and space (PDF)
This study dealt with exclusively using raw received I/Q samples to develop a low-order statistical feature set for typical WSN and ISM band wireless signal classification
Summary
Wireless sensor networks (WSNs) are increasingly being integrated into safety-critical applications such as, for example, the Internet of Things (IoT), which has evolved WSNs into essential elements of current technology These networks are being adopted across a diverse application space, including missile defense [1], health care (wireless body area networks) [2], remote patient monitoring [3], space exploration [4,5], aerospace, surveillance, industrial sensing, control and monitoring systems [6]. This broad array of machine-tomachine and machine-to-people deployments creates new challenges relating to security, spectral coexistence and threat identification, due to radio spectrum variations and diverse fluctuating operating environments. These critical applications will, likely, continue to embrace WSNs in the modern cost-centered age, due to enabling easier design, installation and maintenance, while simultaneously providing new deployment opportunities
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