Research On Reliable 3-D Charging Scheduling For Wireless Rechargeable Sensor Networks

Wireless sensor networks(WSNs)is a communication network that is self-organized by wireless communication between sensor nodes,which constitutes one of the core technologies of the Internet of Things(IoTs).The application scenarios involve a wide range of fields such as military,medicine,agriculture,and geology.In order to cope with the energy bottleneck problem of sensor nodes,wireless rechargeable sensor networks(WRSNs)emerged in recent years.Scholars have conducted research on various problems of WRSNs,which provides an effective solution to the energy bottleneck problems faced by IoTs applications.Research on WRSNs is still limited to 2D networks,and more and more application scenarios are based on 3D networks.The urgency of 3D network research is mainly reflected in two aspects.First,the charging scheduling theory of 2D networks cannot fully meet the network charging needs of 3D scenes.The actual application scenarios based on the IoTs still cannot effectively solve the energy bottleneck of nodes.Second,the entire WRSNs system lacks the theoretical support of 3D scenes,making wireless charging technology still unable to be put into practical application and production environments on a large scale.This paper divides wireless charging technology into two categories: one-to-one charging model and one-to-many charging model,and respectively establishes 3D networks while ensuring reliability.In the one-to-one charging model,this work adopts unmanned aerial vehicles(UAV)and mobile charger(MC)to cooperatively charge sensors.Four algorithms including task selection,infeasibility test,task updating,and cooperation mechanism among regions are proposed.In the one-to-many charging model,the objective is to maximize sensor energy under a single-UAV 3D network,and a cost-efficient charging scheduling approximation algorithm is proposed to ensure network reliability.Test-bed experiments and simulations are conducted to verify the effectiveness and reliability algorithms.Compared with traditional methods,our charging scheduling schemes show great advantages in minimizing total sleep time and maximizing charged energy for sensors.Furthermore,correctness and effectiveness of this work are validated definitely.