Mobility Identification Utilizing Deep Learning for LoRaWAN Location-Based Services

Abstract

This letter introduces a device mobility identification (DMI) method required for Long-Range Wide Area Networks location-based services, including both mobile and stationary applications. The proposed DMI, trained under various channel conditions, can determine the mobility status of the device in real-time without motion sensors or positioning systems. The training data given to the DMI consisted of packet loss, signal-to-noise ratio, and received signal strength indicator histories recorded at the network server. The performance evaluation provided insights into the generalization capability of the proposed deep learning model for the DMI across both known and unknown environments. Furthermore, the proposed method improves mobility identification performance, achieving an accuracy of 87.30%, as compared with 74.17% of the existing support vector machine approach in an online test assuming a real-time pedestrian monitoring service.