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Title: Performance analysis of the IEEE 802.15.4e TSCH-CA algorithm under a non-ideal channel

Authors: Soraya Touloum; Louiza Bouallouche-Medjkoune; Djamil Aissani; Celia Ouanteur

Addresses: Research Unit LaMOS (Modelling and Optimization of Systems), Faculty of Exact Sciences, University of Bejaia, 06000 Bejaia, Algeria ' Research Unit LaMOS (Modelling and Optimization of Systems), Faculty of Exact Sciences, University of Bejaia, 06000 Bejaia, Algeria ' Research Unit LaMOS (Modelling and Optimization of Systems), Faculty of Exact Sciences, University of Bejaia, 06000 Bejaia, Algeria ' Research Unit LaMOS (Modelling and Optimization of Systems), Faculty of Exact Sciences, University of Bejaia, 06000 Bejaia, Algeria

Abstract: The TSCH Collision Avoidance (TSCH-CA) algorithm has been implemented by the IEEE 802.15.4e amendment to decrease the probability of repeated collisions in the packet retransmission in the Industrial Wireless Sensor Networks (IWSNs). This paper proposes a two-dimensional Markov chain model to evaluate the performances of the TSCH-CA algorithm when only shared links are used under non-ideal channel conditions. The accuracy of this model has been verified through Monte Carlo simulations. Based on the proposed model, the expressions of different performance metrics that include retransmission probability, data packet loss rate, reliability, energy consumption, normalised throughput and average access delay have been obtained. Furthermore, a comparative study between TSCH-CA and the unslotted CSMA-CA of IEEE 802.15.4 under a non-ideal channel has been provided. Numerical results reveal that the TSCH-CA performances are clearly affected by channel errors when using only shared links under a noisy environment.

Keywords: IWSNs; IEEE 802.15.4e; TSCH-CA; non-ideal channel; modelling; Markov chains; performances analysis.

DOI: 10.1504/IJWMC.2020.104765

International Journal of Wireless and Mobile Computing, 2020 Vol.18 No.1, pp.1 - 15

Received: 09 Aug 2018
Accepted: 07 May 2019

Published online: 28 Jan 2020 *

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