Skip to main content
U.S. flag

An official website of the United States government

Official websites use .gov
A .gov website belongs to an official government organization in the United States.

Secure .gov websites use HTTPS
A lock ( ) or https:// means you’ve safely connected to the .gov website. Share sensitive information only on official, secure websites.

In-Vehicle Software Defined Networking: An Enabler for Data Interoperability

Published

Author(s)

Khalid Halba, Charif Mahmoudi

Abstract

Future transportation systems evolve new in-vehicle network designs are required to handle the heterogeneous data generated by different Electronic Control Modules (ECUs). Enabling interaction between these data sources can trigger innovation and the emergence of new smart features significantly impacting upon security and riders experience. The interoperability between the ECUs is of high value in the context of autonomous transportation systems. Indeed, it enables different technologies to collaborate for achieving complex tasks. Without this interoperability, features like radar system connected to the Media Oriented Systems Transport bus (MOST) cannot trigger the electronic stability control connected to the Controller Area Network (CAN). These features allow the car to mitigate a high-risk situation using existing modules. In this work, we propose a Software Defined Network (SDN) approach that enables in-vehicle data sources interoperability that allows ECUs to share a medium. The benefits of the proposed approach are backed by the implementation of a relevant use case and the generation of simulation results.
Conference Dates
April 9-11, 2018
Conference Location
Lakeland, FL
Conference Title
2018 2nd International Conference on Information System and Data Mining

Keywords

In-vehicle Networks, Software Defined Networking, Interoperability.

Citation

Halba, K. and Mahmoudi, C. (2018), In-Vehicle Software Defined Networking: An Enabler for Data Interoperability, 2018 2nd International Conference on Information System and Data Mining, Lakeland, FL, [online], https://doi.org/10.1145/3206098.3206105 (Accessed October 8, 2024)

Issues

If you have any questions about this publication or are having problems accessing it, please contact reflib@nist.gov.

Created June 27, 2018, Updated January 27, 2020