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 ( \(\textrm_2\) ), 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.

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Data Availability

The datasets generated during and/or analysed during the current study are not publicly available, but are available from the corresponding author on reasonable request.

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Funding

The authors declare that no funds, grants, or other support were received during the preparation of this manuscript.

Author information

Authors and Affiliations

  1. Department of Computer Science and Engineering, Indian Institute of Information Technology Kottayam, Valavoor, Kerala, India Koppala Guravaiah & Niharika Naik Dharavathu
  2. Department of Computer Science and Engineering, National Institute of Technology Warangal, Hanamkonda, India Venkanna Udutalapally
  3. Department of Computer Science and Engineering, National Institute of Technology Tiruchirappalli, Tiruchirappalli, India Leela Velusamy Rangaraj
  1. Koppala Guravaiah