| Author: | Wang, Yuhao |
| Title: | On-road feature detection and data dissemination in vehicular ad-hoc networks |
| Advisors: | Ho, Ivan (EIE) |
| Degree: | M.Sc. |
| Year: | 2017 |
| Subject: | Hong Kong Polytechnic University -- Dissertations Vehicular ad hoc networks (Computer networks) Intelligent transportation systems |
| Department: | Department of Electronic and Information Engineering |
| Pages: | xiv, 122 pages : color illustrations |
| Language: | English |
| Abstract: | Within the smart city framework, information dissemination in vehicular ad-hoc networks (VANET) is attracting considerable interest in both the research community and industry. Efficient data dissemination has long been a problem in ad-hoc networks. In VANET, the problem is even more challenging given the high mobility of vehicles and intermittent network connectivity. Realistic modelling of the mobility patterns of vehicles (instead of random models like Random Waypoint or Manhattan Models in previous works) is also important for accurate performance evaluation. In this thesis, the transmission of image-based feature detection data in VANET is studied. Specifically, we propose a license plate detection module that can identify vehicles under different lighting conditions (e.g., daytime and nighttime), and applied fountain coding in the application layer to largely reduce the average transmission delay and save network bandwidth. The proposed algorithms are rigorously evaluated under a semi-realistic simulation of an inter-bus communication network in the Mong Kok district in Hong Kong. In practice, the proposed system can be applied to bus lane occupancy control. For example, when a vehicle illegally occupies the bus lane, buses nearby can help recognize the vehicle and transmit the detected results through VANET to patrol cars for the enforcement. |
| Rights: | All rights reserved |
| Access: | restricted access |
Files in This Item:
| File | Description | Size | Format | |
|---|---|---|---|---|
| 991022131146403411.pdf | For All Users (off-campus access for PolyU Staff & Students only) | 8.6 MB | Adobe PDF | View/Open |
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