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Vehicular Cloud for Smart Driving Using Internet of Things.

S Vijayarangam1, J Megalai1, Sivakumar Krishnan2

  • 1Priyadarshini Engineering College, Vaniyambadi, Tamil Nadu, India.

Journal of Medical Systems
|October 19, 2018
PubMed
Summary
This summary is machine-generated.

Vehicular cloud reliability is enhanced by optimizing vehicle numbers and movement. This research analyzes traffic flow and queuing to suggest alternate routes, aiming to alleviate traffic congestion.

Keywords:
Internet of thingsRandom direction mobility modelStochastic investigationVehicular cloudVehicular cloud computing

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Area of Science:

  • Computer Science
  • Traffic Engineering
  • Network Reliability

Background:

  • Traffic congestion is a significant problem in developed countries.
  • Vehicular clouds offer potential for improving traffic management.
  • Reliability of vehicular clouds depends on vehicle accessibility and movement patterns.

Purpose of the Study:

  • To investigate stochastic attributes of traffic using a cloud-based approach.
  • To analyze vehicle movement and queuing dynamics.
  • To propose a model for traffic management and suggest alternate routes.

Main Methods:

  • Stochastic investigation of traffic attributes within a street segment.
  • Utilizing a traffic model for free-flow movement analysis (activity density, dwell time, vehicle quantity).
  • Employing a queuing model for queue flow analysis (queue length, time in queue).

Main Results:

  • Identified key parameters influencing traffic flow and queuing.
  • Quantified vehicle movement characteristics in free-flow and queuing scenarios.
  • Demonstrated the potential for a cloud-based system to manage traffic attributes.

Conclusions:

  • Optimizing vehicle numbers and movement enhances vehicular cloud reliability.
  • The proposed model provides insights into traffic dynamics.
  • The research outcome can help reduce traffic congestion by suggesting alternate routes.