SDN based communications privacy-preserving architecture for VANETs using fog computing

Vehicular Ad-hoc Networks (VANETs) have become the one of the most, reassuring, encouraging and the quickest developing subsets of Mobile Ad-hoc Networks (MANETs). They are involved smart vehicles, Onboard Units (OBUs), and Roadside Units (RSUs) which convey by the temperamental wireless media. Other than lacking frameworks, forwarding elements move with various speeds. Hence, this postpones setting up dependable start to finish correspondence ways and having the proficient on the information move. As such, VANETs have the differing framework concerns and security troubles in getting the openness of universal accessibility, secure correspondence, exchanges, and notoriety the executives framework which impact the trust in coordinated effort and game plan between convenient frameworks. A new VANETs structure for secure communications is offered, and it contains two sub modules, the Software Defined Network (SDN) and the Fog Computing (FC). The SDN provides scalability, programmability complaisance, and the global information about the network, while the FC provides sensitive and location-aware services that meet future VANETs requirements. Additionally, we used the HABE encryption method to provide central security and offers secure and reliable communication. The proposed framework can address major VANETs problems by delivering Vehicle to Infrastructure (V2I) and Vehicle to Vehicle (V2V) communications. In this work, we demonstrated the communication example as well as the use case examples. We provide communication and computation performance along with security analysis. We provide the simulation results, in which we calculate the communication delays and the probability density function, and also compute the moving and running time with respect to the communication radius, that shows the method performs outclass for V2V and the V2I communications in terms of running time, moving distance, communication delays, storage, communication load and responce time. (c) 2020 Elsevier Inc. All rights reserved.