Secure Intelligent Vehicular Network Using Fog Computing

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ID: 20305
2019
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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.
Reference Key
erskine2019secureelectronics Use this key to autocite in the manuscript while using SciMatic Manuscript Manager or Thesis Manager
Authors Erskine, Samuel Kofi;Elleithy, Khaled M.;
Journal Electronics
Year 2019
DOI
DOI not found
URL
https://www.mdpi.com/2079-9292/8/4/455
Keywords Keywords not found

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