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Wireless TSN Extension to Enable Deterministic Connectivity: Implementation and Performance Evaluation
  • Mohamed Seliem ,
  • Ahmed Zahran ,
  • Dirk Pesch
Mohamed Seliem
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Ahmed Zahran
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Dirk Pesch
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Wi-Fi promises to revolutionize wireless connectivity through the integration of time-sensitive networking (TSN) capabilities, ensuring low latency and unmatched reliability within license-exempt spectrum bands. This study delves into the seamless amalgamation of TSN functionalities with Wi-Fi, with a particular focus on its implications for Internet of Things (IoT) applications, notably industrial automation and collaborative robots. In this paper, we examine diverse network configurations to assess their impact on latency and reliability in industrial automation scenarios. Furthermore, the efficacy of prioritization, preemption, and credit-based shaping policies is evaluated to optimize time-sensitive traffic management and enhance overall performance. The findings highlight Wi-Fi’s enhanced suitability for industrial automation, showcasing how collaborative robots can leverage reduced latency and real-time communication to achieve heightened precision, safety, and seamless human-robot interaction. By considering several network setups and traffic shaping policies, this paper provides invaluable insights for industrial practitioners and researchers, shedding light on the potential impact of Wi-Fi in industrial automation and collaborative robotics domains.
This publication has been accepted to be presented in IEEE 9th World Forum on Internet of Things and therefore published in the conference proceedings. © 2023 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.  For the purpose of Open Access, the authors have applied a CC BY public copyright licence to any Author Accepted Manuscript version arising from this submission-https:// creativecommons.org/licenses/by/4.0/ .