Abstract
VANET (vehicular ad hoc network) has a main objective to improve driver safety and traffic efficiency. The intermittent exchange of real-time safety message delivery in VANET has become an urgent concern due to DoS (denial of service) and smart and normal intrusions (SNI) attacks. The intermittent communication of VANET generates huge amount of data which requires typical storage and intelligence infrastructure. Fog computing (FC) plays an important role in storage, computation, and communication needs. In this research, fog computing (FC) integrates with hybrid optimization algorithms (OAs) including the Cuckoo search algorithm (CSA), firefly algorithm (FA), firefly neural network, and the key distribution establishment (KDE) for authenticating both the network level and the node level against all attacks for trustworthiness in VANET. The proposed scheme is termed “Secure Intelligent Vehicular Network using fog computing” (SIVNFC). A feedforward back propagation neural network (FFBP-NN), also termed the firefly neural, is used as a classifier to distinguish between the attacking vehicles and genuine vehicles. The SIVNFC scheme is compared with the Cuckoo, the FA, and the firefly neural network to evaluate the quality of services (QoS) parameters such as jitter and throughput.
Highlights
It is noticeable that the automation industry has substantially improved in the last couple of years
8, wevalidation can see that the proposed scheme calculates both the training data y latency and of jitter that is theSIVNFC
The expectation is that the proposed Secure Intelligent Vehicular Network using fog computing” (SIVNFC) scheme will decrease with each epoch, which means that our model is predicting proposed scheme will decrease with each which means that our model is predicting the value of y more accurately as we continue toepoch, train the model
Summary
It is noticeable that the automation industry has substantially improved in the last couple of years. FC integration with OAs including KDE sharing can be useful for implementing VANET safety applications, since these schemes have the capability to ensure efficient storage, time sensitivity, trustworthiness, and intelligence in real-time information delivery agendas and QoS in Intelligent Transportation systems. To address these concerns, in this paper, we propose a “Secure Intelligent Vehicular Network using fog computing” (SIVNFC) scheme for FC integration and hybrid OAs deployment including CSA, FA, firefly neural networks, and KDE/authentication to detect the network level and node level security in VANET against DoS, SNI, and other forms of attacks.
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