IoV-Fog-Assisted Framework for Accident Detection and Classification

Abstract

The evolution of vehicular research into an effectuating area like the Internet of Vehicles (IoV) was verified by technical developments in hardware. The integration of the Internet of Things (IoT) and Vehicular Ad-hoc Networks (VANET) has significantly impacted addressing various problems, from dangerous situations to finding practical solutions. During a catastrophic collision, the vehicle experiences extreme turbulence, which may be captured using Micro-Electromechanical systems (MEMS) to yield signatures characterizing the severity of the accident. This study presents a three-layer design, with the data collecting layer relying on a low-power IoT configuration that includes GPS and an MPU 6050 placed on an Arduino Mega. The fog layer oversees data pre-processing and other low-level computing operations. With its extensive computing capabilities, the farthest cloud layer carries out Multidimensional Dynamic Time Warping (MDTW) to identify accidents and maintains the information repository by updating it. The experimentation compared the state-of-the-art algorithms such as Support Vector Machine (SVM), K-Nearest Neighbor (KNN), and Random Forest Tree (RFT) using threshold-based detection with the proposed MDTW clustering approach. Data collection involves simulating accidents via VirtualCrash for training and testing, whereas the IoV circuitry would be utilized in actual real-life scenarios. The proposed approach achieved an F1-Score of 0.8921 and 0.8184 for rear and head-on collisions.