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.

针对物联网中通信无线协作对于设备上的干扰检测和识别 (IDI) 方式与商用的 IoT 硬件的限制相冲突的问题，本文提出了一个基于单次干扰爆发级别的干扰识别的轻量级和高效的方法，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中的干扰信号，并根据所采用的监督学习分类器的适当性能和复杂性权衡，针对不同 IoT 平台的计算能力进行处理。其中，实验结果在异构干扰场景中，显示了 90%-97% 的识别准确率，且分类器的性能与机器学习方法相当，能够在无线传感器网络 - COTS 硬件上实时运行。