Vehicular cloud computing networks: availability modelling and sensitivity analysis

Abstract

Vehicle ad hoc networks (VANETs) have emerged to make traffic more efficient and intelligent. Road side units (RSUs) can act as sensors and as a provider of route information for vehicles. RSUs have processing, storage, and communication capabilities. However, RSUs can suffer from peak requests, non-functional data demands and unavailability. To overcome this deficiency, cloud computing can act as an additional resource, processing part of the requests, named vehicular cloud computing (VCC). This paper uses stochastic Petri nets (SPNs) and reliability block diagrams (RBD) to assess a VCC architecture's availability and reliability with multiple RSUs. Two sensitivity analyses were performed which have identified the model's components that have the most significant impact. In addition to a base model, extended models with greater redundancy were also proposed. The base model has obtained A = 97.68%, and the extended model obtained A = 99.19%. Therefore, the models aim to help network administrators plan more optimised VANET architectures, reducing failures.