Research On Charging Strategy For Uav-aided Wireless Sensor Networks

The energy limitation of sensor nodes is an important issue in wireless sensor networks(WSNs).It is related to the lifetime of WSNs.In recent years,it has attracted a wide interest of scholars from all over the world to use unmanned aerial vehicle(UAV)for charging nodes.This thesis focuses on the charging strategy for UAV-aided WSNs.According to the remaining power of the sensor nodes and the UAV’s onboard energy,the UAV’s trajectory is optimized under the condition that the WSNs work normally.Thereby,it can save energy as much as possible,charge more sensor nodes,and improve the charging efficiency of UAV.The main contributions of this thesis are as follows:(1)A deterministic charging strategy for single UAV-aided WSNs is proposed.This strategy comprehensively considers the energy efficiency of UAV and the remaining time of the sensor nodes.Its goal is to meet the node charging needs as much as possible and maximize the energy efficiency of UAV.This strategy first uses the K-means clustering algorithm for clustering,and then proposes an algorithm that maximizes the minimum remaining time to plan the UAV’s trajectory.Simulation results show that,compared with the existing two deterministic charging strategies,the proposed strategy improves the energy efficiency of UAV and also improves the survival rate of nodes.(2)A non-deterministic charging strategy for single UAV-aided WSNs is proposed,which aims to maximize the charging success rate of UAV in a cruise cycle.This strategy also uses Kmeans clustering algorithm to cluster the nodes that have charging requests,and then uses the recursive algorithm to construct the UAV’s trajectory which has largest number of nodes.Simulation results show that,compared with the two existing non-deterministic charging strategies,the proposed strategy improves the charging success rate of UAV.(3)A charging strategy for multi-UAV-aided WSNs is proposed,which schedules multiple UAVs to charge sensor nodes.We first classify the remaining time of the nodes,select the node with the lowest remaining time level as the charging object,and then use the improved K-means clustering algorithm to cluster the nodes that need to be charged.Each cluster arranges a UAV,and finally each UAV chooses the appropriate charging strategy for single UAV to charge.Simulation results show that compared with the existing multi-UAV charging strategy,the proposed strategy improves the charging success rate in large-scale WSNs.