학술논문

EnGINE: Developing a Flexible Research Infrastructure for Reliable and Scalable Intra-Vehicular TSN Networks
Document Type
Conference
Source
2021 17th International Conference on Network and Service Management (CNSM) Network and Service Management (CNSM), 2021 17th International Conference on. :530-536 Oct, 2021
Subject
Communication, Networking and Broadcast Technologies
Computing and Processing
Multimedia systems
Layout
Ethernet
Real-time systems
Topology
Reliability
Low latency communication
Language
ISSN
2165-963X
Abstract
Driver assistance, self-driving, and multimedia systems have two common implications: increasing demand on network bandwidth and the need for more powerful computation nodes. As a result, intra-vehicular networks (IVNs) change their layout. They are built around central nodes connected to the rest of the vehicle via Ethernet. The usage of Ethernet presents a challenge, as it lacks support for deterministic behavior by design. The solution is found within the IEEE Time-Sensitive Networking (TSN) standards, introducing real-time, low-latency, and deterministic communication into the Ethernet ecosystem. These new networked systems need to be thoroughly evaluated with IVN requirements in mind. To assess numerous configurations of IVN setups, in this work, we introduce a novel Environment for Generic In-vehicular Networking Experiments — EnGINE. It allows, among many others, repeatable, reproducible, and replicable TSN experiments with high precision and flexibility, which is not possible to run using proprietary solutions. EnGINE is based exclusively on commercial off-the-shelf components and is orchestrated by a flexible Ansible framework. This approach allows us to configure various topologies emulating realistic IVNs behavior, which is challenging using simulations. Based on available related work, we further address the challenges found in the IVNs. We derive additional requirements for experiments in the TSN domain and present our approach to fulfill them in an experimental setting. We believe that EnGINE provides the ideal environment for TSN network experiments.