{"title":"利用数据扩增改进基于 WSN 的数据集,用于 TSCH 协议性能建模","authors":"Melchizedek Alipio","doi":"10.1016/j.future.2024.107540","DOIUrl":null,"url":null,"abstract":"<div><div>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.</div></div>","PeriodicalId":55132,"journal":{"name":"Future Generation Computer Systems-The International Journal of Escience","volume":"163 ","pages":"Article 107540"},"PeriodicalIF":6.2000,"publicationDate":"2024-09-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Improving WSN-based dataset using data augmentation for TSCH protocol performance modeling\",\"authors\":\"Melchizedek Alipio\",\"doi\":\"10.1016/j.future.2024.107540\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><div>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.</div></div>\",\"PeriodicalId\":55132,\"journal\":{\"name\":\"Future Generation Computer Systems-The International Journal of Escience\",\"volume\":\"163 \",\"pages\":\"Article 107540\"},\"PeriodicalIF\":6.2000,\"publicationDate\":\"2024-09-21\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Future Generation Computer Systems-The International Journal of Escience\",\"FirstCategoryId\":\"94\",\"ListUrlMain\":\"https://www.sciencedirect.com/science/article/pii/S0167739X24005041\",\"RegionNum\":2,\"RegionCategory\":\"计算机科学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"COMPUTER SCIENCE, THEORY & METHODS\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Future Generation Computer Systems-The International Journal of Escience","FirstCategoryId":"94","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S0167739X24005041","RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"COMPUTER SCIENCE, THEORY & METHODS","Score":null,"Total":0}
引用次数: 0
摘要
本研究通过引入一种可行的机器学习(ML)方法,解决了无线传感器网络(WSN)中时隙信道跳频(TSCH)协议中数据集不足的问题。本研究采用的数据集来自实际的传感器节点实施,确保了数据的真实性和相关性。为了消除过拟合,在建模阶段使用了变异自动编码器(VAE)和生成对抗网络(GAN)算法来增强数据,同时还采用了随机森林(RF)和人工神经网络(ANN)算法。结果表明,通过实施数据增强技术,ML 模型的性能有了显著提高。对各种 ML 模型的比较分析表明,通过 GAN 技术增强的 RF 模型更具优势。该模型增强了对 TSCH 延迟的预测能力,凸显了其在网络协议性能建模方面的功效。
Improving WSN-based dataset using data augmentation for TSCH protocol performance modeling
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.
期刊介绍:
Computing infrastructures and systems are constantly evolving, resulting in increasingly complex and collaborative scientific applications. To cope with these advancements, there is a growing need for collaborative tools that can effectively map, control, and execute these applications.
Furthermore, with the explosion of Big Data, there is a requirement for innovative methods and infrastructures to collect, analyze, and derive meaningful insights from the vast amount of data generated. This necessitates the integration of computational and storage capabilities, databases, sensors, and human collaboration.
Future Generation Computer Systems aims to pioneer advancements in distributed systems, collaborative environments, high-performance computing, and Big Data analytics. It strives to stay at the forefront of developments in grids, clouds, and the Internet of Things (IoT) to effectively address the challenges posed by these wide-area, fully distributed sensing and computing systems.