RSITS: Road Safety Intelligent Transport System in Deep Federated Learning Assisted Fog Cloud Networks

Grønli, Tor‐Morten; Lakhan, Abdullah; Younas, Muhammad

doi:10.1007/978-3-031-39764-6_2

Cited by 4 publications

(2 citation statements)

References 24 publications

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“…The game theory analysis methods are used to track the clear flow of data between authorized vehicles based on predetermined thresholds. These studies [14]- [18] suggested federated learning-based ways to guess the speeds, locations, and risks of collisions between vehicles in spread-out nodes. Federated learning distributes training and validation of malware and suspicious activities among nodes with the minimum overhead and processing time.…”

Section: Related Workmentioning

confidence: 99%

“…Recently, many centralized and decentralized cybersecurity IoV frameworks based on blockchain technology [4], [5], [7]- [10] have been suggested for cooperated vehicles in roadside services. The intrusion detection system (IDS), fire-less, secure access control, and trust management secure vehicles' private and sensitive information in fog cloud networks [11]- [14]. Eventbased secure services, such as accurate traffic information and the location of vehicles, are determined efficiently.…”

Section: Introductionmentioning

confidence: 99%

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Augmented IoT Cooperative Vehicular Framework Based on Distributed Deep Blockchain Networks

Lakhan,

Mohammed,

Zebari

et al. 2024

IEEE Internet Things J.

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This paper presents the augmented Internet of Things (AIoT) framework for cooperatively distributed deep blockchain-assisted vehicle networks. AIoT framework splits the vehicle application into various tasks while executing them on different computing nodes. The vehicle application has different constraints, such as security, time, and accuracy, which are considered during processing them on parallel computing nodes (e.g., fog and cloud). We propose a partitioned AIoT scheme, dividing vehicular tasks into local and remote tasks. The objective is to minimize delays and efficiently execute urgent tasks such as vehicle, pedestrian, and traffic signals on local vehicles. The existing blockchain technologies suffer from many security issues, such as anonymous node issues and malware attacks in blockchain blocks. This is why we present the combined deep convolutional neural network (DCNN)-assisted proof-oftrust miner (PoTM) scheme. It safely handles tasks in different blocks. The smart contract is a human-written piece of code in blockchain technologies so that malicious code can be integrated This article was co-funded by the European Union under the REFRESH -Research Excellence For REgion Sustainability and High-tech Industries project number CZ.

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Section: Related Workmentioning

confidence: 99%