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 

机构地区：[1]Department of Computer Science,Rajkiya Engineering College,Kannauj,209732,India [2]Department of Computer Science and Engineering,School of Engineering,Harcourt Butler Technical University,Kanpur,208001,India

出　　处：《Data Science and Management》2024年第3期189-196,共8页数据科学与管理（英文）

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 healthcare.To address the limitations imposed by inadequate resources,energy,and network scalability,this type of network relies heavily on data aggregation and clustering algorithms.Although 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 applications.This paper presents an approach based on state-of-the-art machine-learning methods.In 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 reduction.The primary objectives of this study are to enhance the service life of a network,reduce energy usage,and improve data aggregation efficiency.We evaluated the proposed methodology using data collected from sensors deployed in agricultural fields for crop monitoring.Our 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.

关 键 词：Wireless sensor network Principal component analysis(PCA) Reinforcement learning Data aggregation 

分 类 号：TP3[自动化与计算机技术—计算机科学与技术]