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Hybrid duty cycle algorithm for industrial WSNs using machine learning

Abstract : Wireless Sensor Networks (WSNs) are used to monitor physical or environmental conditions. Due to energy and bandwidth constraints, wireless sensors are prone to packet loss during communication. To overcome the physical constraints of WSNs, there is an extensive renewed interest in applying data-driven machine learning methods. In this paper, we present a missioncritical surveillance system model for industrial environments. In our proposed system, a decision tree algorithm is installed on a centralized server to predict the wireless channel quality of the wireless sensors. Based on the machine-learning algorithm directives, wireless sensor nodes can proactively adapt their duty cycle to mobility, interference and hidden terminal. Extensive simulation results validate our proposed system. The prediction algorithm shows a classification accuracy exceeding 73%, which allows the duty cycle adaptation algorithm to significantly minimize the delay and energy cost compared to using pure TDMA or CSMA/CA protocols.
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https://hal.archives-ouvertes.fr/hal-02962613
Contributor : Jose Manuel Rubio Hernan <>
Submitted on : Friday, October 9, 2020 - 1:24:33 PM
Last modification on : Saturday, January 30, 2021 - 3:19:57 AM

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Charbel Nicolas, Abdel-Mehsen Ahmad, Jose Rubio-Hernan, Gilbert Habib. Hybrid duty cycle algorithm for industrial WSNs using machine learning. SoMMA 2019: 1st Symposium on Machine Learning and Metaheuristics Algorithms, and Applications, Dec 2019, Trivandrum, India. pp.1-15, ⟨10.1007/978-981-15-4301-2_1⟩. ⟨hal-02962613⟩

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