Mobility Prediction for Efficient Resources Management in Vehicular Cloud Computing

Vehicular Cloud Computing (VCC) has become a significant research area recently, due to its potential advantages and applications, especially in the field of Intelligent Transportation Systems (ITS). However, the high mobility of vehicular environment poses crucial challenges to resources allocation and management in VCC, which makes its implementation more complex than conventional clouds. Many works have been introduced to address various issues and aspects of VCC, including resources management and Virtual Machine Migration in vehicular clouds. However, using mobility prediction in VCC has not been studied previously. In this paper, we introduce a novel solution to reduce the effect of resources mobility on the performance of vehicular cloud, using an efficient resources management scheme based on vehicles mobility prediction. This approach enables the vehicular cloud to take pre-planned procedures, based on the output of an Artificial Neural Network (ANN) mobility prediction model. The aim is to reduce the negative impact of sudden changes in vehicles locations on vehicular cloud performance. A simulation scenario is introduced to compare between the performance of our resources management scheme and other resources management approaches introduced in the literature. The simulation environment is based on Nagel-Shreckenberg cellular automata (CA) discrete model for traffic simulation. Simulation results show that our proposed approach has leveraged the performance of vehicular cloud effectively without overusing available vehicular cloud resources.