Reliable and Secure Vehicular Fog Service Provision

Yao, Yingying; Chang, Xiaolin; Mišić, Jelena; Mišić, Vojislav B.

doi:10.1109/jiot.2018.2855718

Cited by 43 publications

(38 citation statements)

References 29 publications

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“…We set different system parameters, and compare the proposed DQN algorithm performance with simulated annealing algorithm in [16, 20], full LVC calculation and full LVD calculation. ‘Full LVC computing’ means that all users choose to offload to the fog server with macro base stations for computing.…”

Section: Simulation Experiments and Results Analysismentioning

confidence: 99%

Overall computing offloading strategy based on deep reinforcement learning in vehicle fog computing

Tan¹,

Zhu

2020

J. eng.

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Section: Simulation Experiments and Results Analysismentioning

confidence: 99%

Overall computing offloading strategy based on deep reinforcement learning in vehicle fog computing

Tan¹,

Zhu

2020

J. eng.

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“…A high level diagram considered in the proposed work is depicted in Fig. 1 and is inspired from the work carried out in [9], [19]. As shown in the figure, the considered vehicular fog computing environment primarily consists of the following entities which actively participate in provisioning the designed authentication and key exchange solution in the considered VFC setup based on blockchain and ECC.…”

Section: System Modelmentioning

confidence: 99%

“…For example, Kang et al [8] proposed a privacy-preserved pseudonym scheme to address the location privacy issues in VFC. Likewise, Yao et al [9] presented a three-layered framework to ensure the reliability and security of VFC while preserving its performance. Considering the importance of vehicular crowdsensing in transportation, Wei et al [10] proposed a fog-based privacypreserving scheme for conditional road surface monitoring.…”

Section: Introductionmentioning

confidence: 99%

Blockchain-Based Lightweight Authentication Mechanism for Vehicular Fog Infrastructure

Kaur

Garg

Kaddoum

et al. 2019

2019 IEEE International Conference on Communications Workshops (ICC Workshops)

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With the increasing development of advanced communication technologies, vehicles are becoming smarter and more connected. Due to the tremendous growth of various vehicular applications, a huge amount of data is generated through advanced on-board devices and is deemed critical to improve driving safety and enhance vehicular services. However, cloud based models often fall short in applications where latency and mobility are critical. In order to fully realize the potential of vehicular networks, the challenges of efficient communication and computation need to be addressed. In this direction, vehicular fog computing (VFC) has emerged which extends the concept of fog computing to conventional vehicular networks. It is a geographically distributed paradigm that has the potential to conduct time-critical and data-intensive tasks by pushing intelligence (i.e. computing resources, storage, and application services) in the vicinity of end vehicles. However secure and reliable transmission are of significant importance in highly-mobile vehicular networks in order to ensure the optimal Quality of Service (QoS). In this direction, several authentication mechanisms have been proposed in the literature but most of them are found unfit due to absence of decentralization, anonymity, and trust characteristics. Thus, an effective cross-datacenter authentication and key-exchange scheme based on blockchain and elliptic curve cryptography (ECC) is proposed in this paper. Here, the distributed ledger of blockchain is used for maintaining the network information while the highly secure ECC is employed for mutual authentication between vehicles and road side units (RSUs). Additionally, the proposed scheme is lightweight and scalable for the considered VFC setup. The performance evaluation results against the existing state-of-the-art reveal that the proposed scheme accomplishes enhanced security features with reduced computational and communicational overheads. Further, its extensive evaluation on the widely applicable Automated Validation of Internet Security Protocols and Applications (AVISPA) tool guarantee its safeness against different attack vectors.

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“…Yingying et.al. [8] proposed a system of making use of idle parked vehicles to implement vehicular fog, as a means of sharing computing resources with nodes in a vehicular network. This approach however is not suitable for surveillance purposes, as surveillance is most required on roads to help prevent live accidents.…”

Section: Relevant Workmentioning

confidence: 99%

Autonomous Vehicular Surveillance using License Plate Recognition over Cloud Computing Architecture

Dayana,

Chanda,

Vinayak

et al. 2019

IJEAT

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License Plate Recognition (LPR) is the extraction and identification of licence plate numbers from license plates. The extraction process requires ample image pre-processing using normalization, gray scaling and edge removal techniques. These extracted plates can then be identified using image processing techniques such as neural networks and support vector machines. These license plates are captured using stationary video cameras, which extracts images from their feed as inputs into the image extraction algorithm. For the purposes of vehicular surveillance, these cameras are inefficient, as a lot of them will be required to monitor vehicles effectively. Hence there is a need for a larger scale model to carry out effective vehicular surveillance. For this purpose, the cameras embedded in self driving cars are utilised as replacements to stationary video cameras. These cameras have to advantage of being constantly mobile, hence being able to carry out a larger scale of surveillance. These cameras capture meaningful images of license plates from their video feed, and upload these images to the cloud using a Vehicular Cloud Computing (VCC) architecture. This centralized cloud carries out the image extraction and image processing tasks. The identified license plates can be used to monitor the cars they belong to. The cloud compares them to a database of license plates that are flagged by law enforcement. If the license plate is found to be flagged, then the respective law enforcement authorities are notified of the location of the car. If the plate belongs to a car with a history of misbehavior, the car capturing the plate is informed of thus, making it easier to safely navigate around the problematic driver.

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Reliable and Secure Vehicular Fog Service Provision

Cited by 43 publications

References 29 publications

Overall computing offloading strategy based on deep reinforcement learning in vehicle fog computing

Overall computing offloading strategy based on deep reinforcement learning in vehicle fog computing

Blockchain-Based Lightweight Authentication Mechanism for Vehicular Fog Infrastructure

Autonomous Vehicular Surveillance using License Plate Recognition over Cloud Computing Architecture

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