Committee Chair

Kandah, Farah

Committee Member

Skjellum, Anthony; Ward, Michael; Liang, Yu

Department

Dept. of Computer Science and Engineering

College

College of Engineering and Computer Science

Publisher

University of Tennessee at Chattanooga

Place of Publication

Chattanooga (Tenn.)

Abstract

The modern world is constantly in a state of technological revolution. Everyday some new technological idea, invention, or threat emerges. With modern computer software and hardware advancements, we have the emergence of more internet-enabled devices - or, Internet of Things (IoT) devices. We can now create large networks with any device to gather real-time information about an environment. In conjunction, modern car companies across the board have a push from public demand for a fully-autonomous car. In order to accomplish autonomy safely and effectively, Vehicular Ad-Hoc Networks (VANETs) must be established for a local group of cars and their environment to ensure all correct and relevant information is communicated throughout the network. The data collected in a VANET can be passed to machine learning models in order to predict possible conditions and detect anomalies. This thesis explores different ways of clustering local groups of vehicles along with machine learning algorithms to predict where vehicles are likely to be and detect false or impossible information.

Degree

M. S.; A thesis submitted to the faculty of the University of Tennessee at Chattanooga in partial fulfillment of the requirements of the degree of Master of Science.

Date

12-2020

Subject

Cluster analysis--Computer programs; Machine learning; Vehicular ad hoc networks (Computer networks)

Keyword

clustering; machine learning; prediction; regression; vehicular networks

Document Type

Masters theses

DCMI Type

Text

Extent

x, 69 leaves

Language

English

Rights

http://rightsstatements.org/vocab/InC/1.0/

License

http://creativecommons.org/licenses/by-sa/4.0/

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