Research On UAV Auxiliary Information Acquisition Mechanism In Backscatter Communication Network

As the key technology of wireless communication,the wireless sensor network is limited by the energy supply equipment,and the need for regular replacement of batteries or timely charging will bring extremely high maintenance costs when sensors are deployed on a large scale.Due to its low energy consumption,low cost,and easy adjustment,backscatter communication technology will play an important role in the era of the Internet of Things.The integration of backscatter communication technology into traditional wireless sensor networks enables sensor nodes that originally consume energy.The system overhead can be well optimized by transmitting information by way of backscattering.As a small,lightweight and highly maneuverable new tool,UAVs are gradually being used in various fields and play a key role in advancing the development of wireless communications.In the backscatter communication network,the UAV can be used as both a data receiver and a radio frequency signal source.The UAV assists the ground base station to collect information,which can improve the transmission efficiency of the system.When collecting the information of backscattering nodes,by adjusting the dispatching strategy of the UAV,the indicators such as energy consumption and time consumption of the system can be well reduced.In this thesis,based on the network scenarios with different node densities,two information collection mechanisms based on the optimized UAV dispatch strategy are proposed.In this thesis,the mechanism of UAV auxiliary information acquisition in sparse backscatter communication network is studied.The nodes are sparsely distributed in the entire network area.During the information collection process,the ground base station in the center transmits radio frequency signals to the nodes within the coverage area.After receiving the radio frequency signals,the nodes upload the information they perceive by backscattering.back to the base station.However,for nodes within the coverage of the base station but far away from the base station and nodes outside the coverage of the base station,huge energy consumption will be generated when uploading information.For this,the central base station is assisted by the introduction of drones to collect these two types of nodes.Information.First,the entire network scene is partitioned,in which the inner area is the base station responsible area,and the outer area is the drone-assisted area.In the area under the responsibility of the base station,the nodes transmit the collected information back to the base station directly through backscattering.In the drone-assisted area,the drone with the battery starts from the base station,hovers near the node,and then the node scatters the radio frequency signal sent by the drone to send information to the drone.By optimizing the UAV’s dispatch strategy,the transmission energy consumption of the entire system can be reduced and the transmission efficiency can be improved.In this regard,this thesis first analyzes the optimization scheme of genetic algorithm for this problem,and then proposes an optimization algorithm for UAV dispatch strategy based on attention mechanism,and builds a network model for training.Finally,through simulation verification,the algorithm based on the attention mechanism performs well when optimizing the energy consumption,time consumption and other indicators of information collection.This thesis studies the UAV auxiliary information collection mechanism in the dense backscatter communication network.In dense scenarios,due to the increase in the number of nodes,the overhead of base stations and UAVs in collecting information is greatly increased.At this time,simply dividing the target area into two parts,the inner and outer parts,will also bring about a large energy consumption.In this regard,the cluster head position is determined by introducing the wireless sensor network clustering routing algorithm,and then the position where the drone hovers when collecting information is determined.Node backscattering collects information,which can reduce the transmission overhead of the network.How to optimize the positioning of cluster heads will directly affect the efficiency of UAV-assisted collection of node information,which in turn affects the energy efficiency of the entire system.In this regard,this thesis first expounds how to determine the cluster head size of the system,and analyzes the energy consumption of the UAV in the process of collecting information,and then based on the backscattering network scenario in this thesis,the LEACH algorithm is first improved.Then,the method of cluster head determination based on FCA algorithm is given.Finally,this thesis proposes a method of using virtual cluster head to optimize cluster head positioning.The simulation results show that the proposed algorithm has obvious effect on the energy consumption optimization of the system compared with the comparison algorithm.