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MediaTUM
Conference object . 2016
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https://doi.org/10.1109/vnc.20...
Article . 2016 . Peer-reviewed
Data sources: Crossref
DBLP
Conference object . 2022
Data sources: DBLP
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Formally verifiable modeling of in-vehicle time-sensitive networks (TSN) based on logic programming

Authors: Morteza Hashemi Farzaneh; Sina Shafaei; Alois C. Knoll;

Formally verifiable modeling of in-vehicle time-sensitive networks (TSN) based on logic programming

Abstract

Increasing number of in-vehicle sensors, actuators and controllers involved in novel applications such as autonomous driving, requires new communication technologies to fulfill heterogeneous non-functional requirements such as latency, bandwidth and reliability. Time-Sensitive Networking (TSN) is a set of new standards in development by Institute of Electrical and Electronics Engineers (IEEE) defined to support mixed criticality based on Ethernet technology. This technology has recently raised significant attention of automotive domain. However, the mutual influence of application requirements in relation to TSN standards still remains a complex problem to master. For instance, considering an existing complex automotive network, an engineer has to carefully analyze the possible effects of adding new sensors on other existing critical applications. The network has to be configured such that the fulfilling of all requirements is verified. Targeting this problem, a modeling approach based on Logic Programming (LP) is developed to support more efficient configuration and verification process with focus on in-vehicle TSN networks.

Keywords

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  • BIP!
    Impact byBIP!
    selected citations
    These citations are derived from selected sources.
    This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
    16
    popularity
    This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
    Top 10%
    influence
    This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
    Top 10%
    impulse
    This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
    Top 10%
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selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
16
Top 10%
Top 10%
Top 10%
Green