Date Approved


Graduate Degree Type


Degree Name

Computer Information Systems (M.S.)

Degree Program

School of Computing and Information Systems

First Advisor

Dr. Xiang Cao

Second Advisor

Dr. Gregory Wolffe

Third Advisor

Dr. Jie Du

Academic Year



Intelligent Transportation System (ITS) has been an important research area in building the foundational infrastructures of self-driving vehicles and improving traffic efficiency of future transportation systems. Scientists have been hoping to incorporate intelligence into traditional transportation systems to help reduce the risks, accident rates, traffic congestion, and even environmental emissions.

There are many research works that have been focused on the communication part of ITS, such as vehicular networks, which collect data from vehicles and send it to the cloud for analysis. In the vehicular networks, Roadside Unit (RSU) is a key infrastructure as an intermediate layer between the vehicles and the cloud. Inspired by the concept of edge computing, RSU can work as devices to buffer the raw data and perform certain data processing before uploading data to the cloud.

RSU has limited data storage capacities and signal coverage range. Hence, it is important to carefully deploy RSU in the vehicular networks for better efficiency. Many existing research works have proposed insightful solutions for placement strategies of RSU, most of which have primarily focused on the perspective of communication, without considering the data storage capacities of RSU. In this thesis, we jointly consider these two factors and offer more comprehensive solutions for solving two specific goals: (1) Given signal coverage range and data storage capacities of RSUs, it finds out the minimal number of RSUs with their deployment locations based on the traffic density, and (2) Given a fixed number of RSUs with their signal coverage range, data storage capacities and traffic density, it deploys RSU in appropriate locations to provide the best coverage. Simulation results showed our solutions improve the performance.

Available for download on Monday, December 18, 2023