scholarly journals Secure Roadside Unit Hotspot Against Eavesdropping Based Traffic Analysis in Edge Computing Based Internet of Vehicles

IEEE Access ◽  
2018 ◽  
Vol 6 ◽  
pp. 62371-62383 ◽  
Author(s):  
Xumin Huang ◽  
Rong Yu ◽  
Miao Pan ◽  
Lei Shu
Keyword(s):  
Traffic Analysis ◽  
Edge Computing ◽  
Internet Of Vehicles ◽  
Roadside Unit

Dynamic server placement in edge computing toward Internet of Vehicles

2021 ◽  
Author(s):  
Bowen Shen ◽  
Xiaolong Xu ◽  
Lianyong Qi ◽  
Xuyun Zhang ◽  
Gautam Srivastava
Keyword(s):  
Edge Computing ◽  
Internet Of Vehicles ◽  
Server Placement

Multi-Source Temporal Knowledge Graph Embedding for Edge Computing Enabled Internet of Vehicles

2021 ◽  
Author(s):  
Haoyang Shi ◽  
Yulan Zhang ◽  
Zhanyang Xu ◽  
Xiaolong Xu ◽  
Lianyong Qi
Keyword(s):  
Graph Embedding ◽  
Edge Computing ◽  
Knowledge Graph ◽  
Internet Of Vehicles

scholarly journals Cost-Effective Resource Sharing in an Internet of Vehicles-Employed Mobile Edge Computing Environment

Symmetry ◽  
2018 ◽  
Vol 10 (11) ◽  
pp. 594 ◽  
Author(s):  
Tri Nguyen ◽  
Tien-Dung Nguyen ◽  
Van Nguyen ◽  
Xuan-Qui Pham ◽  
Eui-Nam Huh
Keyword(s):  
Video Streaming ◽  
Resource Sharing ◽  
Vehicular Network ◽  
Edge Computing ◽  
Mixed Integer ◽  
Mobile Users ◽  
Vehicle Speed ◽  
Mobile Edge Computing ◽  
Internet Of Vehicles ◽  
The Cost

By bringing the computation and storage resources close proximity to the mobile network edge, mobile edge computing (MEC) is a key enabling technology for satisfying the Internet of Vehicles (IoV) infotainment applications’ requirements, e.g., video streaming service (VSA). However, the explosive growth of mobile video traffic brings challenges for video streaming providers (VSPs). One known issue is that a huge traffic burden on the vehicular network leads to increasing VSP costs for providing VSA to mobile users (i.e., autonomous vehicles). To address this issue, an efficient resource sharing scheme between underutilized vehicular resources is a promising solution to reduce the cost of serving VSA in the vehicular network. Therefore, we propose a new VSA model based on the lower cost of obtaining data from vehicles and then minimize the VSP’s cost. By using existing data resources from nearby vehicles, our proposal can reduce the cost of providing video service to mobile users. Specifically, we formulate our problem as mixed integer nonlinear programming (MINP) in order to calculate the total payment of the VSP. In addition, we introduce an incentive mechanism to encourage users to rent its resources. Our solution represents a strategy to optimize the VSP serving cost under the quality of service (QoS) requirements. Simulation results demonstrate that our proposed mechanism is possible to achieve up to 21% and 11% cost-savings in terms of the request arrival rate and vehicle speed, in comparison with other existing schemes, respectively.

Capsule Network Distributed Learning with Multi-Access Edge Computing for the Internet of Vehicles

2021 ◽  
Vol 59 (8) ◽  
pp. 52-57
Author(s):  
Jie Xu ◽  
F. Richard Yu ◽  
Jingyu Wang ◽  
Qi Qi ◽  
Haifeng Sun ◽  
...  
Keyword(s):  
Distributed Learning ◽  
Edge Computing ◽  
The Internet ◽  
Internet Of Vehicles ◽  
Multi Access

scholarly journals Edge Computing and Blockchain for Quick Fake News Detection in IoV

Sensors ◽  
2020 ◽  
Vol 20 (16) ◽  
pp. 4360 ◽  
Author(s):  
Yonggang Xiao ◽  
Yanbing Liu ◽  
Tun Li
Keyword(s):  
Road Safety ◽  
Virtual Machines ◽  
Negative Impact ◽  
Edge Computing ◽  
Fake News ◽  
Network Computing ◽  
Internet Of Vehicles ◽  
Traffic Efficiency ◽  
Computing Framework ◽  
Road Side Units

The dissemination of false messages in Internet of Vehicles (IoV) has a negative impact on road safety and traffic efficiency. Therefore, it is critical to quickly detect fake news considering news timeliness in IoV. We propose a network computing framework Quick Fake News Detection (QcFND) in this paper, which exploits the technologies from Software-Defined Networking (SDN), edge computing, blockchain, and Bayesian networks. QcFND consists of two tiers: edge and vehicles. The edge is composed of Software-Defined Road Side Units (SDRSUs), which is extended from traditional Road Side Units (RSUs) and hosts virtual machines such as SDN controllers and blockchain servers. The SDN controllers help to implement the load balancing on IoV. The blockchain servers accommodate the reports submitted by vehicles and calculate the probability of the presence of a traffic event, providing time-sensitive services to the passing vehicles. Specifically, we exploit Bayesian Network to infer whether to trust the received traffic reports. We test the performance of QcFND with three platforms, i.e., Veins, Hyperledger Fabric, and Netica. Extensive simulations and experiments show that QcFND achieves good performance compared with other solutions.

A Blockchain-based Containerized Edge Computing Platform for the Internet of Vehicles

2020 ◽  
pp. 1-1
Author(s):  
Laizhong Cui ◽  
Ziteng Chen ◽  
Shu Yang ◽  
Zhongxing Ming ◽  
Qi Li ◽  
...  
Keyword(s):  
Edge Computing ◽  
The Internet ◽  
Internet Of Vehicles ◽  
Computing Platform
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