Energy sampling-based interference detection and identification (IDI) methods collide with the limitations of commercial off-the-shelf (COTS) IoT hardware. Moreover, long sensing times, complexity and inability to track concurrent interference strongly inhibit their applicability in most IoT deployments. Motivated by the increasing need for on-device IDI for wireless coexistence, we develop a lightweight and efficient method targeting interference identification already at the level of single interference bursts. Our method exploits real-time extraction of envelope and model-aided spectral features, specifically designed considering the physical properties of signals captured with COTS hardware. We adopt manifold supervised-learning (SL) classifiers ensuring suitable performance and complexity trade-off for IoT platforms with different computational capabilities. The proposed IDI method is capable of real-time identification of IEEE 802.11b g n, 802.15.4, 802.15.1 and Bluetooth Low Energy wireless standards, enabling isolation and extraction of standard-specific traffic statistics even in the case of heavy concurrent interference. We perform an experimental study in real environments with heterogeneous interference scenarios, showing 90 -97 burst identification accuracy. Meanwhile, the lightweight SL methods, running online on wireless sensor networks-COTS hardware, ensure sub-ms identification time and limited performance gap from machine-learning approaches.
Using specialized hardware, such as WLAN cards and SDRs, for protocol-free IDI has also been investigated in many studies, e.g., @cite_19 @cite_17 @cite_22 and references therein. As this hardware can ensure high sampling frequency and high-resolution I Q data, the benefit of more complex classifiers (e.g., deep learning-based @cite_22 ) increases and higher accuracy is generally achieved. However, we have shown that, even with limited sensing resolution and lightweight supervised learning, COTS IoT nodes can reach the same-level of accuracy in real-time.
@cite_19 @cite_22 @cite_17
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In this paper, we propose Airshark -- a system that detects multiple non-WiFi RF devices in real-time and using only commodity WiFi hardware. To motivate the need for systems like Airshark, we start with measurement study that characterizes the usage and prevalence of non-WiFi devices across many locations. We then present the design and implementation of Airshark. Airshark extracts unique features using the functionality provided by a WiFi card to detect multiple non-WiFi devices including fixed frequency devices (e.g., ZigBee, analog cordless phone), frequency hoppers (e.g., Bluetooth, game controllers like Xbox), and broadband interferers (e.g., microwave ovens). Airshark has an average detection accuracy of 91-96 , even in the presence of multiple simultaneously active RF devices operating at a wide range of signal strengths (-80 to -30 dBm), while maintaining a low false positive rate. Through a deployment in two production WLANs, we show that Airshark can be a useful tool to the WLAN administrators in understanding non-WiFi interference. This paper looks into the modulation classification problem for a distributed wireless spectrum sensing network. First, a new data-driven model for automatic modulation classification based on long short term memory (LSTM) is proposed. The model learns from the time domain amplitude and phase information of the modulation schemes present in the training data without requiring expert features like higher order cyclic moments. Analyses show that the proposed model yields an average classification accuracy of close to 90 at varying signal-to-noise ratio conditions ranging from 0 dB to 20 dB. Further, we explore the utility of this LSTM model for a variable symbol rate scenario. We show that a LSTM based model can learn good representations of variable length time domain sequences, which is useful in classifying modulation signals with different symbol rates. The achieved accuracy of 75 on an input sample length of 64 for which it was not trained, substantiates the representation power of the model. To reduce the data communication overhead from distributed sensors, the feasibility of classification using averaged magnitude spectrum data and on-line classification on the low-cost spectrum sensors are studied. Furthermore, quantized realizations of the proposed models are analyzed for deployment on sensors with low processing power.