Joint Energy Predication and Gathering Data in Wireless Rechargeable Sensor Network

作     者：I.Vallirathi S.Ebenezer Juliet 

作者机构：Department of Computer Science and EngineeringPET Engineering CollegeVallioor627117India Department of Computer Science and EngineeringVV College of EngineeringThisayanvilai627657India 

出 版 物：《Computer Systems Science & Engineering》 (计算机系统科学与工程（英文）)

年 卷 期：2023年第44卷第3期

页      面：2349-2360页

核心收录：

学科分类：0810[工学-信息与通信工程] 0809[工学-电子科学与技术（可授工学、理学学位）] 08[工学] 0714[理学-统计学（可授理学、经济学学位）] 0701[理学-数学] 0812[工学-计算机科学与技术（可授工学、理学学位）] 

主　　题：WSNs MCs WRSN K-means algorithm shortest hamiltonian cycle regression analysis 

摘      要：Wireless Sensor Network(WSNs)is an infrastructure-less wireless net-work deployed in an increasing number of wireless sensors in an ad-hoc *** the sensor nodes could be powered using batteries,the development of WSN energy constraints is considered to be a key *** wireless sensor networks(WSNs),wireless mobile chargers(MCs)conquer such issues mainly,energy *** proposed work is to produce an energy-efficient recharge method for Wireless Rechargeable Sensor Network(WRSN),which results in a longer lifespan of the network by reducing charging delay and maintaining the residual energy of the *** this algorithm,each node gets sorted using the K-means technique,in which the data gets distributed into various *** mobile charges execute a Short Hamiltonian cycle opposite direction to reach each cluster’s anchor *** position of the anchor points is calculated based on the energy distribution using the base *** this case,the network will act as a spare MC,so that one of the two MCs will run out of energy before reaching the *** the current tours of the two MCs terminate,regression analysis for energy prediction initiates,enabling the updating of anchor points in the upcoming *** on thefindings of the regression-based energy prediction model,the recommended algorithm could effectively refill network energy.