A Scalable and Quick-Response Software Defined Vehicular Network Assisted by Mobile Edge Computing

By Jianqi Liu, Jiafu Wan, Bi Zeng, Qin-ruo Wang, Houbing Song, Meikang Qiu. IEEE Communications Magazine, 2017

317 citations recorded by the source corpus.

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

Future Intelligent and Secure Vehicular Network Toward 6G: Machine-Learning Approaches (2020, background): “[117] proposed that the SDN can be embedded with mobile-edge computing (MEC) to enable scalable and quick response for the heterogeneous vehicular network.”
Task Offloading in Vehicular Edge Computing Networks: A Load-Balancing Solution (2020, background): “[31] proposed an architecture that integrated different types of access technologies in order to reduce the processing delay of the delay-sensitive applications.”
Networking and Communications in Autonomous Driving: A Survey (2019, background): “ciency of resources [172], and its programmability makes the network more flexible [173].”
Narrow Band Internet of Things (2017, background): “In [61], in order to provide low-latency and high-reliability communication, an SDN-enabled network architecture assisted by MEC [62], which integrates different types of access technologies, is proposed.”
Smart Factory of Industry 4.0: Key Technologies, Application Case, and Challenges (2018, background): “Therefore, it is crucial to achieve network scalability and adaptive transmission [55], [56].”
Deep Reinforcement Learning for Offloading and Resource Allocation in Vehicle Edge Computing and Networks (2019, background): “To reduce both the latency and the transmission cost of the computation ofﬂoading, a cloud-based MEC ofﬂoading framework is proposed for vehic-ularnetworksin[23].”