BIVFN: Blockchain-Enabled Intelligent Vehicular Fog Networks

Gaba, Priyanka; Raw, Ram Shringar

doi:10.1007/978-981-19-4162-7_3

Cited by 1 publication

(1 citation statement)

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“…A VFN deals with the mobility management of vehicles between different fog servers to maintain the quality of service and provide essential solutions to the network [ 43 ]. The components of a VFN, with its functionalities and their connections, are shown in Figure 4 [ 44 ]. Various authors have proposed their architecture, algorithms, and ideas for a VFN to make the system efficient.…”

Section: Preliminary and Proposed Solutionmentioning

confidence: 99%

B-SAFE: Blockchain-Enabled Security Architecture for Connected Vehicle Fog Environment

Gaba,

Raw,

Kaiwartya

et al. 2024

Sensors

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Vehicles are no longer stand-alone mechanical entities due to the advancements in vehicle-to-vehicle (V2V) and vehicle-to-infrastructure (V2I) communication-centric Internet of Connected Vehicles (IoV) frameworks. However, the advancement in connected vehicles leads to another serious security threat, online vehicle hijacking, where the steering control of vehicles can be hacked online. The feasibility of traditional security solutions in IoV environments is very limited, considering the intermittent network connectivity to cloud servers and vehicle-centric computing capability constraints. In this context, this paper presents a Blockchain-enabled Security Architecture for a connected vehicular Fog networking Environment (B-SAFE). Firstly, blockchain security and vehicular fog networking are introduced as preliminaries of the framework. Secondly, a three-layer architecture of B-SAFE is presented, focusing on vehicular communication, blockchain at fog nodes, and the cloud as trust and reward management for vehicles. Thirdly, details of the blockchain implementation at fog nodes is presented, along with a flowchart and algorithm. The performance of the evaluation of the proposed framework B-SAFE attests to the benefits in terms of trust, reward points, and threshold calculation.

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Section: Preliminary and Proposed Solutionmentioning

confidence: 99%