Abstract
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.
Highlights
The message exchanges in Vehicular Ad hoc Network (VANET) help drivers perceive the traffic conditions, adapt driving routes, and avoid potential road hazard scenarios [1]. It seems that the road safety and the traffic efficiency can be achieved in VANET
If we extend the functionality of Road Side Units (RSUs), convert them into Edge Computing Nodes (ECNs), and implement the decision logics on them, all RSUs can collaborate on fake news detection
If we further extend the functionality of RSUs, turn them into blockchain nodes, and put the evidence for an event in the blockchain, every RSU in the network has a complete copy of the up-to-date evidence, from which the accurate evaluation of the event can be derived
Summary
The message exchanges in Vehicular Ad hoc Network (VANET) help drivers perceive the traffic conditions, adapt driving routes, and avoid potential road hazard scenarios [1]. It seems that the road safety and the traffic efficiency can be achieved in VANET. The devices at the edge of the network generate a massive amount of data that needs to be stored and computed at cloud data centers. This consumes much network bandwidth and results in the response latency. The computation and storage services are deployed close to where the data is generated, i.e., the edge devices, to save bandwidth and reduce latency
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
