School of Computer Science and Technology, Chongqing University of Posts and Telecommunications, Chongqing 400065, China.
Sensors (Basel). 2020 Aug 5;20(16):4360. doi: 10.3390/s20164360.
The dissemination of false messages in Internet of Vehicles (IoV) has a negative impact on road safety and traffic efficiency. Therefore, it is critical to quickly detect fake news considering news timeliness in IoV. We propose a network computing framework Quick Fake News Detection (QcFND) in this paper, which exploits the technologies from Software-Defined Networking (SDN), edge computing, blockchain, and Bayesian networks. QcFND consists of two tiers: edge and vehicles. The edge is composed of Software-Defined Road Side Units (SDRSUs), which is extended from traditional Road Side Units (RSUs) and hosts virtual machines such as SDN controllers and blockchain servers. The SDN controllers help to implement the load balancing on IoV. The blockchain servers accommodate the reports submitted by vehicles and calculate the probability of the presence of a traffic event, providing time-sensitive services to the passing vehicles. Specifically, we exploit Bayesian Network to infer whether to trust the received traffic reports. We test the performance of QcFND with three platforms, i.e., Veins, Hyperledger Fabric, and Netica. Extensive simulations and experiments show that QcFND achieves good performance compared with other solutions.
互联网车辆（IoV）中虚假信息的传播对道路安全和交通效率有负面影响。因此，考虑到 IoV 中新闻的时效性，快速检测假新闻至关重要。本文提出了一种网络计算框架快速虚假新闻检测（QcFND），它利用了软件定义网络（SDN）、边缘计算、区块链和贝叶斯网络技术。QcFND 由两个层次组成：边缘和车辆。边缘由软件定义的路边单元（SDRSUs）组成，它是从传统的路边单元（RSUs）扩展而来的，并承载 SDN 控制器和区块链服务器等虚拟机。SDN 控制器有助于在 IoV 上实现负载均衡。区块链服务器容纳车辆提交的报告，并计算交通事件存在的概率，为过往车辆提供时间敏感的服务。具体来说，我们利用贝叶斯网络来推断是否信任接收到的交通报告。我们使用三个平台（即 Veins、Hyperledger Fabric 和 Netica）来测试 QcFND 的性能。广泛的模拟和实验表明，与其他解决方案相比，QcFND 具有良好的性能。
