Interference Source Identification for IEEE 802.15.4 wireless Sensor Networks Using Deep Learning

Due to the interference issue in unlicensed band, sensor nodes frequently encounter degraded performance or lack of connection. This paper provides a real-time external interference source classification method for an 802.15.4-based wireless sensor network using deep learning. It uses RSSI sampling for collecting training data as well as online test data in an office environment. The output interference source type includes Wi-Fi beacon, different classes of WLAN traffic, BLE iBeacon, and microwave oven. Wireless sniffers are used to help labeling the ground truth of the sample data. We have trained a deep neural network with two hidden convolutional layers using raw RSSI samples as inputs. A micro-level model and a macro-level model are provided to predict the interference source based on the deep learning result. With implementation on both IEEE 802.15.4 SoC and Linux-based system, our experimental results show that the proposed framework can classify the major interference types with high accuracy.