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The FORA European Training Network on Fog Computing for Robotics and Industrial Automation., and . DATE, page 1-6. IEEE, (2023)Real-Time Traffic Guarantees in Heterogeneous Time-sensitive Networks., , , , and . RTNS, page 46-57. ACM, (2022)Quality-Of-Control-Aware Scheduling of Communication in TSN-Based Fog Computing Platforms Using Constraint Programming., , and . Fog-IoT, volume 80 of OASIcs, page 3:1-3:9. Schloss Dagstuhl - Leibniz-Zentrum für Informatik, (2020)Power system dynamic model reduction by means of an iterative SVD-Krylov model reduction method., , and . ISGT, page 1-6. IEEE, (2016)Towards Extensibility-Aware Scheduling of Industrial Applications on Fog Nodes., , , , , and . EDGE, page 67-75. IEEE, (2020)Real-Time Guarantees for Critical Traffic in IEEE 802.1Qbv TSN Networks with Unscheduled and Unsynchronized End-Systems., , , , and . CoRR, (2021)A Decentralized Approach for Determining Configurator Placement in Dynamic Edge Networks., , and . CogMI, page 147-156. IEEE, (2020)Using JitterTime to Analyze Transient Performance in Adaptive and Reconfigurable Control Systems., , , and . ETFA, page 1025-1032. IEEE, (2019)ELISE: A Reinforcement Learning Framework to Optimize the Slotframe Size of the TSCH Protocol in IoT Networks., , , and . IEEE Syst. J., 18 (2): 1068-1079 (June 2024)A Decomposed Deep Training Solution for Fog Computing Platforms., and . SEC, page 423-431. IEEE, (2021)