An Internet of Vehicles (IoV) Access Gateway Design Considering the Efficiency of the In-Vehicle Ethernet Backbone

A vehicular network is composed of an in-vehicle network (IVN) and Internet of Vehicles (IoV). IVN exchanges information among in-vehicle devices. IoV constructs Vehicle-to-X (V2X) networks outside vehicles and exchanges information among V2X elements. These days, in-vehicle devices that require high bandwidth is increased for autonomous driving services. Thus, the spread of data for vehicles is exploding. This kind of data is exchanged through IoV. Even if the Ethernet backbone of IVN carries a lot of data in the vehicle, the explosive increase in data from outside the vehicle can affect the backbone. That is, the transmission efficiency of the IVN backbone will be reduced due to excessive data traffic. In addition, when IVN data traffic is transmitted to IoV without considering IoV network conditions, the transmission efficiency of IoV is also reduced. Therefore, in this paper, we propose an IoV access gateway to controls the incoming data traffic to the IVN backbone and the outgoing data traffic to the IoV in the network environment where IVN and IoV are integrated. Computer simulations are used to evaluate the performance of the proposed system, and the proposed system shows better performance in the accumulated average transmission delay.

[1]  M. H. MacDougall Simulating computer systems: techniques and tools , 1989 .

[2]  Jeonghwan Gwak,et al.  A Review of Intelligent Self-Driving Vehicle Software Research , 2019, KSII Trans. Internet Inf. Syst..

[3]  Jianning Zhao,et al.  Research on In-Vehicle Key Management System under Upcoming Vehicle Network Architecture , 2019, Electronics.

[4]  Cheol Mun,et al.  Intelligent Hybrid Fusion Algorithm with Vision Patterns for Generation of Precise Digital Road Maps in Self-driving Vehicles , 2020, KSII Trans. Internet Inf. Syst..

[5]  Rodrigo Roman,et al.  Mobile Edge Computing, Fog et al.: A Survey and Analysis of Security Threats and Challenges , 2016, Future Gener. Comput. Syst..

[6]  Zhaoxia Xiao,et al.  Hardware Design of Automobile Door with Local Interconnect Network Bus , 2011, 2011 International Conference on Control, Automation and Systems Engineering (CASE).

[7]  Trong-Yen Lee,et al.  Design of a FlexRay/Ethernet Gateway and Security Mechanism for In-Vehicle Networks , 2020, Sensors.

[8]  Tuan-Minh Pham,et al.  Optimization Model and Algorithm for Dynamic Service-Aware Traffic Steering in Network Functions Virtualization , 2018, 2018 IEEE Seventh International Conference on Communications and Electronics (ICCE).

[9]  Seokhoon Kim,et al.  Network virtualization for real-time processing of object detection using deep learning , 2020, Multimedia Tools and Applications.

[10]  Lei Xu,et al.  Real-Time Vehicle Detection Framework Based on the Fusion of LiDAR and Camera , 2020, Electronics.

[11]  Jia Zhou,et al.  A Survey of Intrusion Detection for In-Vehicle Networks , 2020, IEEE Transactions on Intelligent Transportation Systems.

[12]  YiNa Jeong,et al.  The Lightweight Autonomous Vehicle Self-Diagnosis (LAVS) Using Machine Learning Based on Sensors and Multi-Protocol IoT Gateway , 2019, Sensors.

[13]  Mohsen Guizani,et al.  5G Vehicular Network Resource Management for Improving Radio Access Through Machine Learning , 2020, IEEE Access.

[14]  Samarjit Chakraborty,et al.  Performance Analysis of FlexRay-based ECU Networks , 2007, 2007 44th ACM/IEEE Design Automation Conference.

[15]  YiNa Jeong,et al.  A Design of a Lightweight In-Vehicle Edge Gateway for the Self-Diagnosis of an Autonomous Vehicle , 2018 .

[16]  Daeyoung Kim,et al.  Network-Aided Intelligent Traffic Steering in 5G Mobile Networks , 2020, Computers, Materials & Continua.

[17]  Wenchao Xu,et al.  Internet of vehicles in big data era , 2018, IEEE/CAA Journal of Automatica Sinica.

[18]  Hamed Haddadi,et al.  Deep Learning in Mobile and Wireless Networking: A Survey , 2018, IEEE Communications Surveys & Tutorials.

[19]  YiNa Jeong,et al.  An Optimal Driving Support Strategy(ODSS) for Autonomous Vehicles based on an Genetic Algorithm , 2019 .

[20]  Jong Hyuk Park,et al.  Traffic management in the mobile edge cloud to improve the quality of experience of mobile video , 2017, Comput. Commun..

[21]  Qi Zhu,et al.  An Offloading Strategy for Multi-User Energy Consumption Optimization in Multi-MEC Scene , 2020, KSII Trans. Internet Inf. Syst..

[22]  John D'Ambrosia,et al.  Evolution of ethernet standards in the IEEE 802.3 working group , 2013, IEEE Communications Magazine.

[23]  Sungkwon Park,et al.  Time-Sensitive Network (TSN) Experiment in Sensor-Based Integrated Environment for Autonomous Driving , 2019, Sensors.

[24]  Abdallah Shami,et al.  Softwarization, Virtualization, and Machine Learning for Intelligent and Effective Vehicle-to-Everything Communications , 2020, IEEE Intelligent Transportation Systems Magazine.

[25]  Shangguang Wang,et al.  Architecture and key technologies for Internet of Vehicles: a survey , 2017, Journal of Communications and Information Networks.

[26]  Usman Ali Khan,et al.  Multi-Layer Problems and Solutions in VANETs: A Review , 2019, Electronics.

[27]  Rojeena Bajracharya,et al.  Challenges of Future VANET and Cloud-Based Approaches , 2018, Wirel. Commun. Mob. Comput..

[28]  Fatima de L. P. Duarte-Figueiredo,et al.  A 5G V2X Ecosystem Providing Internet of Vehicles † , 2019, Sensors.

[29]  Wang-Cheol Song,et al.  SD-IoV: SDN enabled routing for internet of vehicles in road-aware approach , 2020, J. Ambient Intell. Humaniz. Comput..

[30]  Seokhoon Kim,et al.  A Data Download Method from RSUs using Fog Computing in Connected Vehicles , 2019, Computers, Materials & Continua.

[31]  Sang Hyun Park,et al.  Implementation of Automotive Media Streaming Service Adapted to Vehicular Environment , 2013, MUE.