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Performance modeling of the IEEE 802.15.4e TSCH enabling both shared and dedicated links in industrial WSNs

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Time Slotted Channel Hopping (TSCH) is one of the Medium Access Control methods proposed in the IEEE 802.15.4e standard to deal with the requirements of the industrial Wireless Sensor Networks (WSNs), especially in terms of high reliability and low latency. The key feature of the TSCH method is the combination between a time slotted access with a channel hopping, while considering both shared and dedicated links. The latter are essential for ensuring transmissions without loss and additional delays. Therefore, the objective we are seeking to reach in this paper is to demonstrate the benefit use of dedicated links on industrial WSNs-based 802.15.4e TSCH method. To this end, we propose an analytical model-based Markov chains for the TSCH method taking into account the dedicated links, and we estimate the transmission probability \(\tau \) of a data packet. The latter will be then used to develop others analytical models, in order to derive a number of performance metrics, namely the average access delay, the reliability, the throughput, and the energy consumption. Furthermore, to validate the analytical model, we perform extensive simulations-based Monte-Carlo. Finally, to give credibility to the obtained simulation results, we compute \(95\%\) confidence intervals. Numerical results show that increasing the number of dedicated links reduces significantly the retransmission probability offering best network performances in terms of average access delay, reliability and throughput.

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Authors and Affiliations

  1. Research Unit LaMOS (Modeling and Optimization of Systems), Faculty of Exact Sciences, University of Bejaia, 06000, Bejaia, Algeria

    Soraya Touloum, Louiza Bouallouche-Medjkoune, Mohand Yazid, Mohand Moktefi & Aïssani Djamil

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  1. Soraya Touloum
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Touloum, S., Bouallouche-Medjkoune, L., Yazid, M. et al. Performance modeling of the IEEE 802.15.4e TSCH enabling both shared and dedicated links in industrial WSNs. Computing 104, 859–891 (2022). https://doi.org/10.1007/s00607-021-00990-2

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