Research On Data Collection Methods In Wireless Sensor Networks With Unmanned Aerial Vehicle

With the rapid development of the Internet of Things(Io T),the research on wireless sensor networks(WSNs)has gradually deepened.In large-scale WSNs,many sensor device nodes are randomly deployed in remote areas to collect information or achieve regional monitoring.Sensor nodes not only need to undertake data collection work but also need to forward the collected data,which will consume a large amount of energy from sensor nodes.However,due to volume limitations,sensor nodes can only carry limited batteries.Therefore,how to fully utilize the limited energy of sensor nodes to achieve long-term collection of information within the monitoring area has become the primary challenge faced by many scholars.In order to reduce the energy consumption of sensor nodes and achieve efficient data collection,this thesis introduces unmanned aerial vehicles(UAVs)into WSNs and proposes a data collection strategy for unmanned aerial vehicle assisted sensor networks based on wake-up radio mechanism.In this strategy,some representative nodes in the wireless sensor network are selected,and UAVs fly to the selected nodes to collect data.This strategy can reduce redundancy between sampled data and improve data collection efficiency.In large-scale networks,a drone needs to collect data from multiple selected nodes,that is,the drone simultaneously collects data from multiple sensors within its receiving range.However,the energy of drones is limited,and the amount of data they can collect is also limited.Therefore,the hovering position of drones will affect the amount of data collected.Therefore,in order to further improve the efficiency of sampling data collection,we propose a drone planning path algorithm that can plan the drone flight path and collect the maximum amount of sampling data during the flight process.The research content is summarized as follows:(1)In response to the energy-saving data collection issue of WSNs,drones and sensor nodes carrying wake-up wireless devices are used,that is,when the sensor node turns off the power when it does not need to transmit data to the drone,to reduce the energy consumption of the sensor node in standby mode.When the drone flies over it,it sends energy to the sensor nodes through the wake-up device to wake up the sensors and transmit data to the drone.At the same time,a Sensor Nodes Selection Algorithm(SNSA)for energy-saving data collection is proposed,which considers the correlation between sensor data and the lifespan of the sensor network while ensuring network coverage.Some sensor nodes are selected to effectively reduce redundant data collected by drones.After obtaining the coordinates of the current working node,this algorithm uses the 2-opt(2-optimization)optimization algorithm to calculate the path of the drone.The simulation results show that the proposed unmanned aerial vehicle path planning algorithm can extend the lifespan of the network.(2)To address the problem of maximizing data collection in UAV-assisted wireless sensor networks,this dissertation focuses on improving the data collection efficiency of wireless sensor networks under one-to-many data collection schemes through path planning of UAVs.In this paper,a method is proposed to select out UAV hovering points in the monitoring area.Then.two UAV path planning algorithms are designed that consider the amount of data collected by the UAV at each hovering point,the hovering duration,and the flight duration in the case that the UAV has a fixed operation duration and limited storage capacity.The simulation results show that the proposed algorithms enable the UAV to collect all the data in the monitoring area.And all of them can reduce the number of UAVs to reduce the cost of network deployment.The greedy algorithm outperforms the random algorithm in terms of data collection volume.