Predictive Big Data Collection in Vehicular Networks: A Software Defined Networking Based Approach

Data collection is key issue in vehicular networks since it is vital for supporting many applications in vehicular environments. With the explosive growth of sensing data in urban area, however, strategies for efficient collection of big data in vehicular networks are still far from being well studied. In this paper, we focus on studying this issue and accordingly propose a Software Defined Vehicular Networks (SDVN) architecture. On this architecture, a predictive data collection algorithm is proposed. In this algorithm, packet delivery is fulfilled by cooperative cellular and ad hoc network interfaces, in which collections of big data always adopts ad hoc based multi-hop relaying whenever applicable to forward packets to Road Side Units (RSUs). Cellular networks are used for data uploading only when no multi-hop relaying opportunity is available. Our proposed SDVN architecture enables such efficient cooperative communications, in which predictive routing decisions are made based on real-time network status other than empirical knowledge. Simulation results demonstrate that our algorithm outperforms existing algorithms in terms of packet delivery ratio and transmit efficiency.