通过监督学习的实时干扰识别：在IoT设备中嵌入共存感知
Real-time Interference Identification via Supervised Learning: Embedding Coexistence Awareness in IoT Devices
摘要：
基于能量采样的干扰检测和识别（IDI）方法与商用现成（COTS）物联网硬件的限制相冲突。此外，长的感测时间、复杂性和无法跟踪并发干扰强烈抑制了它们在大多数物联网部署中的适用性。出于对用于无线共存的设备上IDI的日益增长的需求，我们开发了一种轻量级且高效的方法，该方法已经在单个干扰突发的水平上针对干扰识别。我们的方法利用实时提取的包络和模型辅助的频谱特征，专门设计考虑与COTS硬件捕获的信号的物理特性。我们采用多种监督学习（SL）分类器，确保具有不同计算能力的物联网平台具有合适的性能和复杂性权衡。所提出的IDI方法能够实时识别IEEE 802.11b/g/n、802.15.4、802.15.1和蓝牙低功耗无线标准，即使在严重并发干扰的情况下也能够隔离和提取标准特定的流量统计。我们进行了实验研究，在真实环境中的异构干扰的情况下，显示90%-97%的突发识别准确率。同时，轻量级SL方法，运行在无线传感器网络的COTS硬件在线，确保亚ms的识别时间和有限的性能差距，从机器学习方法。
英文摘要：
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.
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