Time-sensitive networking for interlock propagation in the IFMIF-DONES facility Megías Núñez, Carlos Vázquez Rodríguez, Víctor Ros Vidal, Eduardo Díaz Alonso, Antonio Javier Time-Sensitive Networking Interlock Determinism Convergent networks Machine protection system IFMIF-DONES In this study, we have proposed the use of time-sensitive networking (TSN) technologies for the distribution of the interlock signals of the machine protection system of the future IFMIF-DONES particle accelerator, required for implementing the protection mechanisms of the different systems in the facility. Such facilities usually rely on different fieldbus technologies or direct wiring for their transmission, typically leading to complex network infrastructures and interoperability problems. We provide insights of how TSN could simplify the deployment of the interlock network by aggregating all the traffic under the same network infrastructure, whilst guaranteeing the latency and timing constraints. Since TSN is built on top of Ethernet technology, it also benefits from other network services and all its related developments, including redundancy and bandwidth improvements. The main challenge to address is the transmission of the interlock signals with very low latency between devices located in different points of the facility. We have characterized our initial TSN architecture prototype, evaluated the latency and bandwidth obtained with this solution, identified applications to effectively shape the attainable determinism, and found shortcomings and areas of future improvements. 2023-06-14T09:36:04Z 2023-06-14T09:36:04Z 2023-06 journal article C. Megías et al. Time-sensitive networking for interlock propagation in the IFMIF-DONES facility. Fusion Engineering and Design 191 (2023) 113774. [https://doi.org/10.1016/j.fusengdes.2023.113774] https://hdl.handle.net/10481/82439 10.1016/j.fusengdes.2023.113774 eng info:eu-repo/grantAgreement/EC/EURATOM/101052200 http://creativecommons.org/licenses/by/4.0/ open access Atribución 4.0 Internacional Elsevier