Optimizing data aggregation and clustering in Internet of things networks using principal component analysis and Q-learning

作     者：Abhishek Bajpai Harshita Verma Anita Yadav 

作者机构：Department of Computer ScienceRajkiya Engineering CollegeKannauj209732India Department of Computer Science and EngineeringSchool of EngineeringHarcourt Butler Technical UniversityKanpur208001India 

出 版 物：《Data Science and Management》 (数据科学与管理（英文）)

年 卷 期：2024年第7卷第3期

页      面：189-196页

核心收录：

学科分类：08[工学] 0812[工学-计算机科学与技术（可授工学、理学学位）] 

主　　题：Wireless sensor network Principal component analysis(PCA) Reinforcement learning Data aggregation 

摘      要：The Internet of things(IoT)is a wireless network designed to perform specific tasks and plays a crucial role in various fields such as environmental monitoring,surveillance,and *** address the limitations imposed by inadequate resources,energy,and network scalability,this type of network relies heavily on data aggregation and clustering *** various conventional studies have aimed to enhance the lifespan of a network through robust systems,they do not always provide optimal efficiency for real-time *** paper presents an approach based on state-of-the-art machine-learning *** this study,we employed a novel approach that combines an extended version of principal component analysis(PCA)and a reinforcement learning algorithm to achieve efficient clustering and data *** primary objectives of this study are to enhance the service life of a network,reduce energy usage,and improve data aggregation *** evaluated the proposed methodology using data collected from sensors deployed in agricultural fields for crop *** proposed approach(PQL)was compared to previous studies that utilized adaptive Q-learning(AQL)and regional energy-aware clustering(REAC).Our study outperformed in terms of both network longevity and energy consumption and established a fault-tolerant network.