Internet of Things (IoT) Discovery Using Deep Neural Networks

Ephraim Lo, JoHannah Kohl; The IEEE Winter Conference on Applications of Computer Vision (WACV), 2020, pp. 806-814


We present a novel approach to Internet of Things (IoT) discovery using Deep Neural Network (DNN) based object detection. Traditional methods of IoT discovery are based on either manual or automated monitoring of predetermined channel frequencies. Our method takes the spectrogram images that a human analyst visually scans for manual spectrum exploration and applies the state-of-the-art You Only Look Once (YOLO) object detection algorithm to detect and localize signal objects in time and frequency. We focus specifically on the class of signals that employ the Long Range (LoRa) modulation scheme, which uses chirp spread spectrum technology to provide high network efficiency and robustness against both in- and out-of-band interference. Our detection system is designed with scalability for real or near real-time processing capabilities and achieves 81.82% mAP in real-time on a fourth generation mobile Intel CPU without GPU support. Lastly, we present preliminary detection results for other IoT signals including Zigbee, Bluetooth, and Wi-Fi.

Related Material

author = {Lo, Ephraim and Kohl, JoHannah},
title = {Internet of Things (IoT) Discovery Using Deep Neural Networks},
booktitle = {The IEEE Winter Conference on Applications of Computer Vision (WACV)},
month = {March},
year = {2020}