Parallel Vehicular Networks: A CPSS-Based Approach via Multimodal Big Data in IoV
Social network services, Internet of Things, Intelligent vehicles, Cloud computing, Vehicle dynamics, Complex systems
Daniel Felix Ritchie School of Engineering and Computer Science, Electrical and Computer Engineering
Vehicular networks (VNs) have received great attention as one of the crucial supportive techniques for intelligent transportation systems (ITSs). However, the introduction of dynamic and complex human behaviors into VNs makes it a cyber-social-physical system. Thus, artificial systems, computational experiments, parallel executions-based parallel VNs (PVN) are proposed in this paper. The framework of PVN is then designed and presented, its characteristics and applications are demonstrated, and its related research challenges are discussed. PVN uses software-defined artificial VNs for modeling and representation, computational experiments for analysis and evaluation, and parallel execution for control and management. Thus, more reliable and efficient traffic status and ultrahigh data rate communications are obtained among vehicles and infrastructures, which is expected to achieve the descriptive intelligence, predictive intelligence, and prescription intelligence for VNs. The proposed PVN offers a competitive solution for achieving a smooth, safe, and efficient cooperation among connected vehicles in future ITSs.
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Han, Shuangshuang, et al. “Parallel Vehicular Networks: A CPSS-Based Approach via Multimodal Big Data in IoV.” IEEE Internet of Things Journal, vol. 6, no. 1, 2019, pp. 1079–1089. doi: 10.1109/jiot.2018.2867039.