Improving WSN-based dataset using data augmentation for TSCH protocol performance modeling

College

Gokongwei College of Engineering

Department/Unit

Electronics And Communications Engg

Document Type

Article

Source Title

Future Generation Computer Systems

Volume

163

Publication Date

2025

Abstract

This study addresses the problem of inadequate datasets in Time-Slotted Channel Hopping (TSCH) protocol in Wireless Sensor Networks (WSN) by introducing a viable machine learning (ML) approach that explicitly tackles the limitations associated with the scarcity of data samples. The dataset employed in this research is derived from actual sensor node implementations, ensuring authenticity and relevance. To counteract overfitting, Variational Auto-Encoder (VAE) and Generative Adversarial Network (GAN) algorithms are utilized for data augmentation during the modeling phase, alongside the incorporation of Random Forest (RF) and Artificial Neural Network (ANN) algorithms. Results reveal a notable improvement in the performance of the ML models through the implementation of data augmentation techniques. A comparative analysis of various ML models underscores the superiority of the RF model, augmented by the GAN technique. This model exhibits enhanced predictive capabilities for TSCH latency, underscoring its efficacy in modeling network protocol performance.

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Disciplines

Digital Communications and Networking | Systems and Communications

Keywords

Wireless sensor networks; Network performance (Telecommunication)

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