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Crowd-Learning: A Behavior-Based Verification Method in Software-Defined Vehicular Networks With MEC Framework

For the future open 5G Internet of Vehicles (IoV), due to the flexibility and load sharing, the popular network architecture of IoV proposed by many studies is the mobile-edge computing (MEC) framework combining with software-defined networking (SDN). However, under this architecture, moving vehicles and MEC devices are not like the cloud SDN with strong security protection. Thus, identity verification is an important security issue. We find that if the identity credentials of vehicles and infrastructures are obtained by adversaries (i.e., identity theft), the current cryptography-based authentication methods cannot cope with this problem. In this article, we propose a behavior-based verification method, named Crowd-Learning, by utilizing the idea of crowd in software-defined vehicular networks with a MEC framework. In Crowd-Learning, we design an incentive mechanism to stimulate some MEC infrastructures to provide accurate and appropriate amount of data for future correct behavior estimation. Without knowing the model of the dynamic environment, this incentive mechanism needs to apply reinforcement learning to let MEC infrastructures learn how to send data based on the current state. Our Crowd-Learning method verifies vehicles and reduces the verification latency by estimating the vehicle’s behavior in advance. Meanwhile, it verifies infrastructures during the process of reinforcement learning based on the idea of crowd intelligence. The fake infrastructures and anomalous vehicles expose themselves when learning. In experiments, we use the traffic simulation tool, called simulation of urban mobility (SUMO), to generate extensive vehicle traces and evaluate the performance of the Crowd-Learning verification method. The results show that the Crowd-Learning verification method can ensure high verification accuracy for vehicles and infrastructures with satisfying low verification latency.