FogTMDetector - Fog Based Transport Mode Detection using Smartphones

Abstract

A user’s transport mode (e.g., walk, car, etc.) can be detected by using a smartphone. Such devices exist in a great number with enough computation power and sensors to run a classifier (i.e., for transport mode detection). Using a smartphone in a fog environment ensures low latency, high generalization, high accuracy, and low battery consumption. We propose a fog-based real-time (at human time scale) transport mode detection, called FogTMDetector; it consists of a Random Forest classifier trained with magnetometer, accelerometer, and GPS data. The overall accuracy achieved by our system is 93% when detecting 8 different modes (i.e., stationary, walk, bicycle, car, bus, train, tram, and subway). We compared FogTMDetector with another recent system (called EdgeTrans). The comparison results suggest that our solution achieves 10% higher motorized accuracy (i.e., 94.4%) with more fine-grained motorized transport modes (i.e., subway, tram, etc.) thanks to the magnetometer sensor readings. FogTMDetector uses a low sampling rate (1Hz) for logging accelerometer and magnetometer and (every 10 seconds) for GPS to ensure low battery consumption. FogTMDetector is also generalizable as it is robust against variation of users and smartphone positions.