Connected vehicles have huge potential in improving road safety and traffic congestion. The primary aim of this paper is threefold: firstly to present an overview of network models in connected vehicles; secondly to analyze the factors that impact the Quality of Service (QoS) of connected vehicles and thirdly to present initial modelling results on Link QoS. We use the open access Geometry-based Efficient Propagation Model (GEMV2) data to carry out Analysis of Variance, Principal Component Analysis and Classical Multi-Dimensional scaling on the link quality for vehicle-2-vehicle (V2V) and vehicle-2-infrastucture (V2i) data and found that both line of sight and non-line of sight has a significant impact on the link quality. We further carried out modelling using system identification method of the connected vehicle network (CVN) in terms of Link QoS based on the parameters identified by the QoS assessment. We evaluated the CVN in terms of a step response achieving steady-state within 80 seconds for V2V data and 500 seconds for V2i data. The work presented here will further help in the development of CVN prediction model and control for V2V and vehicle-2-anything connectivity.

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International Journal On Advances in Software



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School of Engineering, Computing and Mathematics