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Analysis of Mobile Edge Computing for Vehicular Networks.

Zachary W Lamb¹, Dharma P Agrawal²

¹Center for Distributed and Mobile Computing, EECS Department, University of Cincinnati, P.O. Box 210030, Cincinnati, OH 45221-0030, USA. lambzw@mail.uc.edu.

Sensors (Basel, Switzerland)

|March 17, 2019

Summary

This study introduces a resource allocation architecture for vehicular ad-hoc networks (VANETs) using 5G. An algorithm effectively matches computational tasks to the most suitable edge or cloud resources, optimizing performance for connected vehicles.

Keywords:

VANET cloud computing distributed computing mobile computing wireless networks

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Area of Science:

Intelligent Transportation Systems (ITS)
Wireless Communications
Edge and Cloud Computing

Background:

Vehicular ad-hoc Networks (VANETs) are evolving from DSRC to 5G cellular technology for enhanced connectivity in intelligent transportation systems.
Autonomous vehicles generate massive data, straining on-board computational and storage capacities.
While cloud computing offers solutions, its latency can hinder real-time VANET applications.

Purpose of the Study:

To propose a novel architecture for efficient resource allocation in 5G-enabled VANETs.
To address the challenges of computational and storage demands posed by connected and autonomous vehicles.
To minimize latency and overhead by intelligently distributing tasks to edge and cloud resources.

Main Methods:

Developed a real-time resource evaluation and allocation architecture.
Employed a multi-criteria decision analysis (MCDA) algorithm for resource ranking.
Validated the approach through mathematical evaluation and experiments using a cloud resource test-bed.

Main Results:

The proposed algorithmic ranking of physical resources closely matched experimental outcomes.
Demonstrated the feasibility of delegating tasks to the most appropriate computational resources.
The architecture effectively manages diverse edge and cloud resources for optimal utilization.

Conclusions:

The developed architecture provides an effective solution for dynamic resource allocation in VANETs.
Algorithmic resource management is crucial for supporting the increasing demands of connected vehicles.
This approach enhances the efficiency and performance of intelligent transportation systems.