AbstractVehicular Ad-Hoc Networks (VANET) can, but not limited to provide users with useful traffic and environmental information services to improve travelling efficiency and road safety. The communications systems used in VANET include vehicle-to-vehicle communications (V2V) and vehicle-to-infrastructure communications (V2I). The transmission delay and the energy consumption cost for maintaining good-quality communications vary depending on the transmission distance and transmission power, especially on motorways where vehicles are moving at higher speeds. In addition, in modern transportation systems, electric vehicles are becoming more and more popular, which require a more efficient battery management, this also call for an efficient way of vehicular transmission.
In this project, a cluster-based two-way data service model to provide real-time data services for vehicles on motorways is designed. The design promotes efficient cooperation between V2V and V2I, or namely V2X, with the objective of improving both service and energy performance for vehicular networks with traffic in the same direction. Clustering is an effective way of applying V2X in VANET systems, where the cluster head will take the main responsibility of exchanging data with Road Side Units (RSU) and other cluster members. The model includes local service data collection, data aggregation, and service data downloading. We use SUMO and OMNET++ to simulate the traffic scenarios and the network communications. Two different models (V2X and V2I) are compared to evaluate the performance of the proposed model under different flow speeds. From the results, we conclude that the cluster-based service model outperforms the non-clustered model in terms of service successful ratio, network throughput and energy consumption.
|Date of Award||2017|
|Supervisor||Xiaohong Peng (Supervisor)|
- data service
- energy efficiency
An efficient cluster-based service model for vehicular ad-hoc networks on motorways
Shi, Y. (Author). 2017
Student thesis: Master's Thesis › Master of Science (by Research)