A Road Health Monitoring System Using Sensors in Optimal Deep Neural Network

Road health monitoring is a prominent area of research in the transportation system to ensure a safer and smoother flow of traffic. It helps to determine the type of road, including smooth, rough, zigzag, etc . Though there exists a significant amount of work towards road health monitoring, that requires high-end machine or cloud support to perform road classification. In this paper, we propose a road health monitoring system using sensors. The system learns deep learning based classifiers, which runs on resource constraint devices such as smartphone, to identify the type of the road. The system optimizes the deep neural network model based on the available resources on the resource constraint device. The optimization is solved for selecting the appropriate model version that matches the current resource availability of the device. We also evaluate the proposed system on the collected sensory dataset to study the impact of background services and residual energy of the device on accuracy and inference time.