Research On Routing Performance Of Sparse Vehicular Ad Hoc Networks

Driving safety and transport efficiency have become global problems, with the increasing car sales through years and popularity of private cars worldwide. As the development of Intelligent Transport System (ITS) and the widely application of Internet of Things (IoT) technologies, people realize that the Vehicular Ad Hoc Networks (VANETs) can solve this problem efficiently. VANET is able to provide drivers with realistic information of traffic and car situation by interchanging messages along the road, which guarantees driving safety and improves transport efficiency. However, the sparse environment causes vehicles fail to maintain continuous wireless channel with others, which is difficult for information transmission. And routing protocols in traditional wireless networks fail to ensure the reliability and effectiveness. So this thesis is aimed at new routing strategies for information transmission in sparse VANET.It’s been discovered, after studying lots of current protocols, that high evaluated routing protocols must be designed based on the precise cognition of vehicles mobility. And that is why this thesis firstly studies the mobility models of sparse VANET. Random Velocity mobility model and Semi-Markov Smooth mobility model based on highway are mainly studied for the purpose of universal theoretical results. The quantitative relationships between variables in mobility models are partly defined through derivation. Meanwhile, the Log-Normal distribution characteristic of distance between adjacent vehicles is discovered by analyzing the experiment data which is obtained in simulator software, and the internal connections are verified among distance between adjacent vehicles, average velocity and vehicle density.Two routing strategies for sparse VANET are presented with the basis of mobility analysis results and the idea of opportunistic communication of Delay Tolerant Networks (DTNs), which are named by Forwarding Nodes Selection Strategy based on Location Prediction and Intelligent Replicates Strategy based on Location Cognition, respectively. Along with gradually improved Location Aided Routing (LAR) Protocol, whose apparent advantages have been already approved beyond traditional protocols in wireless networks, the two strategies are both altered on the template of LAR.Comparisons have been made between the two strategies and LAR through three groups of simulations. Packet delivery ratio, average end-to-end delay and routing overheads are involved in the performance comparisons. The result is that the packet delivery ratio and average end-to-end delay of the two strategies performs better than LAR in sparse environment on the price of larger routing overheads. But as the density gets bigger, the advantages degrade and sometimes LAR performs better instead. So we come to a conclusion that the forwarding nodes selection strategy based on location prediction and the intelligent replicates strategy based on location cognition are very suitable and only for sparse VANET.