Jitter Analysis Framework for Controller Area Network in Vehicular Embedded Systems
2024 (English) In: 2024 International Conference on Artificial Intelligence, Computer, Data Sciences and Applications (ACDSA), Institute of Electrical and Electronics Engineers Inc. , 2024Conference paper, Published paper (Refereed)
Abstract [en]
Controller Area Network (CAN) is an ISO-standardized protocol that is extensively used as the onboard real-time network in vehicles. Evaluating and mitigating responsetime jitter experienced by CAN messages is paramount, particularly in real-time applications where timing predictability is crucial. This paper presents a jitter analysis framework for CAN messages. Within this framework, the worst-case responsetime jitter of CAN messages is calculated through pre-runtime analysis. Additionally, a post-runtime analysis of message traces helps ascertain the maximum response-time jitter these messages encounter at runtime. The framework performs a comparative evaluation of the pre-runtime and runtime response-time jitter and then back-propagates insights and guidelines to the systems' designers to mitigate the jitter. To validate this approach, we implement the framework using two prominent industrial tools: Rubus-ICE and CANalyzer. The effectiveness of the framework is further substantiated by modeling and analyzing an industrial use case provided by Volvo Construction Equipment.
Place, publisher, year, edition, pages Institute of Electrical and Electronics Engineers Inc. , 2024.
Keywords [en]
CAN, Controller Area Network, jitter analysis, Construction equipment, Control system synthesis, Controllers, Embedded systems, Process control, Response time (computer systems), Specifications, Analysis frameworks, Controller-area network, Embedded-system, Network messages, Real time network, Run-time analysis, Runtimes, Time jitters, Jitter
National Category
Electrical Engineering, Electronic Engineering, Information Engineering
Identifiers URN: urn:nbn:se:mdh:diva-66465 DOI: 10.1109/ACDSA59508.2024.10467959 Scopus ID: 2-s2.0-85189932392 ISBN: 9798350394528 (print) OAI: oai:DiVA.org:mdh-66465 DiVA, id: diva2:1852400
Conference International Conference on Artificial Intelligence, Computer, Data Sciences, and Applications, ACDSA 2024
Note Conference paper; Export Date: 17 April 2024; Cited By: 0; Correspondence Address: S. Mubeen; Mälardalen University, Västerås, Sweden; email: saad.mubeen@mdu.se; Conference name: 2024 International Conference on Artificial Intelligence, Computer, Data Sciences, and Applications, ACDSA 2024; Conference date: 1 February 2024 through 2 February 2024; Conference code: 198277
2024-04-182024-04-182024-04-18 Bibliographically approved