Vehicular ad hoc networks have emerged as a promising area of research in academic fields. However, it presents a challenge to design a realistic coverage protocol for ve- hicular networks, due to the service requirements, assorted mobility patterns, resource constraints and irregularity of the service area. It is also a challenge to meet a high quality of coverage with a tight deployment budget. In order to resolve these problems, this thesis proposes a resource-constrained coverage protocol with statistical analysis, which aims to consider the application demands, mobility patterns of vehicles, resource limitations, and geometrical attributes of road networks.
We study two types of resource-constrained coverage: the continuous coverage model and the sparse coverage model. We then reduce each model to a Knapsack Constrained Steiner Tree problem and a Maximum Coverage Problem, respectively. Since the two reduced problems are NP-hard, we resolve each of them with the Lagrangian Decompo- sition approach and greedy algorithm. By taking the dimensions of road segments into account, our coverage protocol provides a buffering operation scheme to suit different types of road topology. By discovering hotspots from the historical trace files, the pro- posed protocol is able to depict the mobility patterns and to discover the most valuable regions of a road system. To solve the problems of resource constraint, we provide two variants of continuous coverage and sparse coverage by taking budget constraint and quality constraint into consideration. The comparison with other mature algorithms verifies that our coverage protocol is reliable, and suitable for urban vehicular networks.
| Identifer | oai:union.ndltd.org:LACETR/oai:collectionscanada.gc.ca:OOU.#10393/30913 |
| Date | 16 April 2014 |
| Creators | Cheng, Huang |
| Source Sets | Library and Archives Canada ETDs Repository / Centre d'archives des thèses électroniques de Bibliothèque et Archives Canada |
| Language | English |
| Detected Language | English |
| Type | Thèse / Thesis |
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