A Privacy-Preserving Fog Computing Framework for Vehicular Crowdsensing Networks

Wei, Jiannan; Wang, Xiaojie; Li, Nan; Yang, Guomin; Mu, Yi

doi:10.1109/access.2018.2861430

Cited by 43 publications

(23 citation statements)

References 28 publications

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“…where z indicates number of neighboring nodes and N x (Y) indicates the trust value of node X on node Y. The vehicles that have been identified as trusted nodes interact with the RSUs through the FS to obtain the data in the appropriate order [43]. The proposed SIVNFC scheme utilizes an RSU prevention mechanism whose model is as follows.…”

Section: Node Level Securitymentioning

confidence: 99%

Secure Intelligent Vehicular Network Using Fog Computing

Erskine

Elleithy

2019

Electronics

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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.

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Section: Node Level Securitymentioning

confidence: 99%

Secure Intelligent Vehicular Network Using Fog Computing

Erskine

Elleithy

2019

Electronics

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“…As a means of addressing this issue, fog computing became an extension to cloud computing [16,17]. Fog computing is a process allocation technique that offloads the different sections of the program from the server to the end devices.…”

Section: Introductionmentioning

confidence: 99%

A scalable IoT-fog framework for urban sound sensing

Baucas

Spachos

2020

Computer Communications

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Internet of Things (IoT) is a system of interrelated devices that can be used to allow large-scale collection and analysis of data. However, as it grew, IoT networks were not capable of managing the data from these services. As a result, cloud computing was introduced to address the need for datacentres for IoT networks. As the technology evolved, the demand for a proper means of supporting and managing crowdsensing and real-time data increased, and cloud servers could no longer keep up with the large volumes of incoming data. This demand brought rise to fog computing. It became an extension to the cloud and allowed resources to be allocated around the network effectively. Its integration to IoT reduced the strain towards the cloud servers. However, issues in high power consumption at the end device and data management constraints surfaced. This paper proposes two approaches to alleviate these issues to keep fog computing remain as a reliable option for IoT-related applications. We created an IoT-based sensing framework that used an urban sound classification model. Through active low and high power states and resource reallocation, we created a network configuration. We tested this configuration against IoT frameworks that use the default fog and cloud setups. The results improved the framework's end device power consumption and server latency. Overall, with the proposed framework, fog computing was proven to be capable of supporting a scalable IoT framework for urban sound sensing.

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“…A typical architecture of fog-assisted vehicular sensor networks (F-VSNs) [12][13][14] contains the trusted authority, cloud center, fog nodes, and vehicles. The trusted authority is responsible for generating system parameters, and the registration of all entities (cloud center, fog nodes and vehicles).…”

Section: Introductionmentioning

confidence: 99%

Efficient Privacy-Preserving Data Sharing for Fog-Assisted Vehicular Sensor Networks

Yang

2020

Sensors

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Vehicular sensor networks (VSNs) have emerged as a paradigm for improving traffic safety in urban cities. However, there are still several issues with VSNs. Vehicles equipped with sensing devices usually upload large amounts of data reports to a remote cloud center for processing and analyzing, causing heavy computation and communication costs. Additionally, to choose an optimal route, it is required for vehicles to query the remote cloud center to obtain road conditions of the potential moving route, leading to an increased communication delay and leakage of location privacy. To solve these problems, this paper proposes an efficient privacy-preserving data sharing (EP 2 DS) scheme for fog-assisted vehicular sensor networks. Specifically, the proposed scheme utilizes fog computing to provide local data sharing with low latency; furthermore, it exploits a super-increasing sequence to format the sensing data of different road segments into one report, thus saving on the resources of communication and computation. In addition, using the modified oblivious transfer technology, the proposed scheme can query the road conditions of the potential moving route without disclosing the query location. Finally, an analysis of security suggests that the proposed scheme can satisfy all the requirements for security and privacy, with the evaluation results indicating that the proposed scheme leads to low costs in computation and communication.

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A Privacy-Preserving Fog Computing Framework for Vehicular Crowdsensing Networks

Cited by 43 publications

References 28 publications

Secure Intelligent Vehicular Network Using Fog Computing

Secure Intelligent Vehicular Network Using Fog Computing

A scalable IoT-fog framework for urban sound sensing

Efficient Privacy-Preserving Data Sharing for Fog-Assisted Vehicular Sensor Networks

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