Reliable data transmission for a VANET-IoIT architecture: A DNN approach

Abstract

The challenges and resilience of vehicular ad hoc network (VANET) and deep neural network (DNN) hybrid architectures in terms of reliability in smart cities have attracted much global interest stemming from the rollout of the next generation of intelligent networks. In this paper, we propose a novel distributed DNN (D-DNN) scheme with blockchain to support the Internet of Intelligent Things (IoIT) infrastructure in the VANET environment of the future. In particular, because the communication links between edge nodes are very unstable in VANETs, a new neuro-fuzzy server that serves the dual roles of finding reliable links between edge nodes and performing optimal routing path selection is proposed. Next, a blockchain layer is employed at the edge nodes, which are initially scrutinized before establishing communication links to ensure reliability during data transfer. Then, the proposed D-DNN (PD-DNN) scheme is applied to enhance the performance of the VANET-IoIT architecture by improving the data flow and convergence rate and mitigating erratic variations in output. To address reliability concerns, the coverage probability (CP) metric is investigated as a measure of network connectivity. Furthermore, we present an analysis of the PD-DNN scheme in comparison with the traditional DNN (T-DNN) scheme. Finally, simulation results for VANET-IoIT scenarios show that, subject to data protection and privacy constraints, the CP values corresponding to different communication links are improved to a greater extent under our scheme than under the traditional scheme, demonstrating the feasibility of the proposed scheme.