Benjamin Sliwa

Resource-Efficient Vehicle-to-Cloud Communications Leveraging Machine Learning


Front	Back

ISBN:

978-3-8440-8356-9

Series:

Dortmunder Beiträge zu Kommunikationsnetzen und -systemen
Herausgeber: Prof. Dr.-Ing. C. Wietfeld
Dortmund

Volume:

Keywords:

Machine Learning; Vehicle-to-Cloud; V2X

Type of publication:

Thesis

Language:

English

Pages:

288 pages

Figures:

129 figures

Weight:

428 g

Format:

21 x 14,8 cm

Binding:

Paperback

Price:

49,80 € / 62,30 SFr

Published:

December 2021

Buy:

Download:

Available PDF-Files for this title:

You need the Adobe Reader, to open the files. Here you get help and information, for the download.

These files are not printable.


	Document		Document
	Type		PDF
	Costs		37,35 EUR
	Action		Purchase in obligation and display of file - 8,1 MB (8531267 Byte)
	Action		Purchase in obligation and download of file - 8,1 MB (8531267 Byte)


	Document		Table of contents
	Type		PDF
	Costs		free
	Action		Display of file - 158 kB (161688 Byte)
	Action		Download of file - 158 kB (161688 Byte)

User settings for registered users

You can change your address here or download your paid documents again.

User:	Not logged in.
Actions:	Login / Register Forgotten your password?

Recommendation:

You want to recommend this title?

Review copy:

Here you can order a review copy.

Link:

You want to link this page? Click here.

Export citations:

Text
BibTex
RIS

Abstract:

Vehicular big data is anticipated to become the "new oil" of the automotive industry. Although the novel vehicle-as-a-sensor paradigm will fuel the emergence of innovative crowdsensing-enabled services, the tremendously increased amount of transmitted data represents a massive challenge for the cellular network infrastructure. More dramatically, the complex vehicular radio propagation environments frequently require to reduce the transmission efficiency in favor of more reliable data transfer, ultimately resulting in a wastage of the limited network resources. This thesis focuses on the development and analysis of novel solution approaches that utilize end-edge intelligence mechanisms at the client devices for vehicle-to-cloud data transfer targeted at delay-tolerant applications. For this purpose, supervised, unsupervised, and reinforcement learning methods are brought together to autonomously detect and exploit favorable transmission opportunities. The results of this thesis show that machine learning-based data rate prediction models are well able to account for the complex interplay of the different logical context domains. As a result, they provide the fundamental information for autonomously learning resource-efficient data transfer policies. As pointed out by a comprehensive real world performance evaluation, the apparently selfish goal of data rate maximization contributes to the good of all and allows to improve the intra-cell coexistence through significantly reducing the number of required network resources per data packet.