Date of Award

8-2020

Document Type

Dissertation

Degree Name

Doctor of Philosophy (PhD)

Department

Computer Engineering and Sciences

First Advisor

Carlos Enrique Otero

Second Advisor

Ersoy Subasi

Third Advisor

Susan Earles

Fourth Advisor

Ivica Kostanic

Abstract

Vehicle management technologies deals with the management of critical vehicle information, including location, idle time, speed, and mileage. Such information can always be transferred through a direct vehicle-to-vehicle communication among cars. However, the limitation of this type of design is that it is based on the assumption that vehicles are always served by cellular bases, which is not always the case. For the effective implementation of the Internet-of-Things (IoT) technology in this sector, it is critical to design vehicles with systems that enable them to transmit essential information in the absence of base stations. IoT technologies can then be used to develop mesh communication between devices to replace the need for cellular service. This project proposes models that can be used to design self-reporting systems for vehicles to enhance self-management. The study also compares the proposed models with theoretical models, which show deviations of between 6% and 23%. The overall efficiency of vehicle-to-vehicle (V2V), vehicle-to/from-infrastructure (V2I) or vehicle-to/from-environments can only be attained if there is a reliable exchange of information between the communicating vehicles. Reliable exchange of information also enhances the overall efficiency with which self-driving cars and autonomous vehicle technologies can be implemented. Such systems require not only a variety of IoT systems, but also a series of sensors and nodes for effective transfer of information, the processing of information, and quick decision-making. However, the heterogeneous environments and overall ecosystems pose reliability changes on the information transmitted to be processed by the ecosystem in order to guarantee the safety and functional operation of the ecosystem. This study examines the reliability of the communication model that can support the operation of self-driving cars ecosystem. It also shows semi-empirical energy per bit to noise spectral density, empirical radio propagation models and parameters for driving and transportation environment. These values and models, which are obtained from a combination of the experimental approach and analytical approach of additive white Gaussian noise channel are used to ensure a reliable communication of wireless sensor nodes deployed in the environments for V2V, V2I, and V2X services. Additionally, the values and models are validated in theoretical and semi-analytical simulation scenarios. The results indicate that both techniques are nearly identical. The semi-empirical approach, the proposed models, and values can be used for efficient planning and future deployments of autonomous vehicles and self-driving cars.

Comments

Copyright held by author

Download

Share

COinS