MOVE in ROAD: Multi-objective Vehicle Monitoring Using River Formation Dynamics and Deep Learning Algorithms

These days, a significant portion of the solutions for vehicle Internet of things applications come from wireless sensor networks. This article uses cameras, radio-frequency identification, and ultrasonic sensors to address typical issues with vehicle technology, such as unlawful vehicle use inside a community, vehicle thefts, and vehicle accidents. It also addresses the issue of identifying vehicle pollution parameter values like carbon monoxide (CO) and carbon dioxide (CO2\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\textrm{CO}_2$$\end{document}), providing information about the driver's alcohol consumption, and verifying the driver's eligibility (driving license). The driving license will be used to identify the driver. Deep learning algorithms, such as Multi-Task Cascaded Convolutional Neural Networks and facenet algorithms, can identify driving licenses. The proposed algorithm has an 92% accuracy rate in detecting the driver's face. The proposed system is installed and demonstrated using Micro-controller, Micro-processor and other sensors in real time environment. The River Formation Dynamics based Multi-hop Routing Protocol for Vehicles (RFDMRPV) is used for communication between vehicles. Data collected from the sensors mounted in vehicles are communicated to server utilizing RFDMRPV for storing. Alert the driver, owner of the vehicle and other authorities depending on the acquired sensor results.