A deep learning-based edge-fog-cloud framework for driving behavior management

Among the various reasons behind vehicle accidents, drivers' aggressiveness and distractions play a significant role. Deep learning (DL) algorithms inside a car mobile edge (CME) have been used for driver monitoring and to perform automated decision-making processes. Training and retraining the DL models in resource-constrained CME devices come with several challenges, especially regarding computational and memory space costs. Moreover, training the DL models periodically on representative data nearest to CME without imposing communication overheads on the cloud improves the quality of service (QoS) parameters, such as memory demand, processing time, power consumption, and bandwidth. This paper investigates the deployment of a deep neural network (DNN) model on a cloud-fog-edge computing framework for aggressive driver behavior detection and monitoring. To reach this goal, our framework proposes utilizing effective systems and databases of sensor-based metrics and data, cost-effective wireless networks, cloud-and fog-edge computing technologies, and the Internet. Experimental results of the DNN model showed that the accuracy of detection is improved by 1.84% compared with the current related work without any pre-processing window on data points that come from bio-signal sensors. Moreover, the experimental results of the networking part prove the efficiency and effectiveness of the proposed framework.