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Using Machine Learning in WSNs for Performance Prediction MAC Layer

Using Machine Learning in WSNs for Performance Prediction MAC Layer

El Arbi Abdellaoui Alaoui, Mohamed-Lamine Messai, Anand Nayyar
Copyright: © 2022 |Volume: 16 |Issue: 1 |Pages: 18
ISSN: 1930-1650|EISSN: 1930-1669|EISBN13: 9781683180203|DOI: 10.4018/IJISP.303667
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MLA

Alaoui, El Arbi Abdellaoui, et al. "Using Machine Learning in WSNs for Performance Prediction MAC Layer." IJISP vol.16, no.1 2022: pp.1-18. http://doi.org/10.4018/IJISP.303667

APA

Alaoui, E. A., Messai, M., & Nayyar, A. (2022). Using Machine Learning in WSNs for Performance Prediction MAC Layer. International Journal of Information Security and Privacy (IJISP), 16(1), 1-18. http://doi.org/10.4018/IJISP.303667

Chicago

Alaoui, El Arbi Abdellaoui, Mohamed-Lamine Messai, and Anand Nayyar. "Using Machine Learning in WSNs for Performance Prediction MAC Layer," International Journal of Information Security and Privacy (IJISP) 16, no.1: 1-18. http://doi.org/10.4018/IJISP.303667

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Abstract

To monitor environments, Wireless Sensor Networks (WSNs) are used for collecting data in divers domains such as smart factories, smart buildings, etc. In such environments, different medium access control (MAC) protocols are available to sensor nodes for wireless communications and are of a paramount importance to enhance the network performance. Proposed MAC layer protocols for WSNs are generally designed to achieve a good performance in packet reception rate. Once chosen, the MAC protocol is used and remains the same throughout the network lifetime even if its performance decreases over time. In this paper, we adopt supervised machine learning techniques to predict the performance of CSMA/CA MAC protocol based on the packet reception rate. Our approach consists of three steps: experiments for data collection, offline modeling and performance evaluation. Our analysis shows that XGBoost prediction model is the better supervised machine learning technique to enhance network performance at the MAC layer level. In addition, we use SHAP method to explain predictions.

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