Title

Adaptive Scheduling for Multicluster Time-Triggered Train Communication Networks

Published In

IEEE Transactions on Industrial Informatics

Document Type

Citation

Publication Date

2-1-2019

Abstract

The execution time of conventional incremental off-line schedule approaches for time-triggered networks increases rapidly when networks become larger. When the traffic in a network changes, they need to reschedule all influenced flows once again. Traffic changes at the cluster level involve many data flows. An incremental scheduler cannot react quickly to such changes. We propose an algorithm based on mixed integer linear programming and counterexample guided methodology. Our algorithm can generate adaptive schedule for cluster-level changes of the system. The adaptive schedule can react quickly to the changes during runtime. Our algorithm enhances the incremental schedulers. It allows schedulers to react to changing at both the flow level and the cluster level. Experiments show that our approach is effective. In the scenarios of coupling train consists, our algorithm can generate the schedule table of the train network within a few seconds.

DOI

10.1109/TII.2018.2865760

Persistent Identifier

https://archives.pdx.edu/ds/psu/27801

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