Research On Low Energy Consumption Data Collection And Transmission Scheme Based On UAV

With the continuous reduction of unmanned aerial vehicle(UAV)costs and the equipment miniaturization,UAV has attracted widespread attention at home and abroad.UAV also has the advantages of higher mobility and flexible deployment.In the field of communications,one of its important functions is to assist ground sensor nodes for data collection and transmission in the wild or inaccessible areas.However,the UAV’s energy is limited.It is easy to increase UAV’s energy consumption for frequent data collection.It will reduce the working time of UAV and easy to collect large amounts of redundant data.In order to reduce UAV’s energy consumption and prolong the survival time of the network,how to reduce the number of UAV’s data collection and how to perform redundant data processing are worthy problems to study.Aiming at the problem of high energy consumption for traditional UAV’s data collection scheme and high redundancy of data returned by UAV,this thesis proposes an adaptive energy saving strategy based on back propagation(BP)neural network prediction model for UAV’s data collection period adjustment and an scheme for UAV redundant data processing based on temporal-spatial correlation.In the existing period adjustment scheme,the period change cannot accurately match the change of the environment,and it is easy to collect more redundant data.It will cause extra energy consumption of UAV.The BP neural network prediction model is used to implement data prediction.In the prediction model,through the training and learning of environmental parameters,high-precision prediction results are obtained.The distance between the predicted value and the current period’s true value is calculated according to the Minkowski distance.Evaluating the range of environmental changes in the collection area by distance.Therefore,under different environmental changes,using three-phase strategy to adjust the period size of the UAV data collection.Let the period changes atch the environment changes Achieving the goal of reducing the frequency of UAV’s data collection while ensuring the integrity of the data collected by UAV.At the same time,the UAV’s status is dynamic adjusted according to the period size of the data collection,so that the UAV changes from the flight status to the dormant status at an appropriate time.The purpose is to reduce UAV’s flight time and reduce UAV’s energy consumption.During the data transmission phase,due to the characteristics of stable environment changes the node data variation amplitude is relative small.The similarity of data values collected in each period is high,and the similarity of data between nodes in the same period is high.It is limited to analyze data from a single dimension.This thesis will analyze the data similarity from the time and spatial dimensions.In the time dimension,the collected results of the current period with the previous period are compared by the improved Euclidean distance.If the similarity is high,they are discarded,otherwise they are retained.In the spatial dimension,the data processed in the time dimension is used to implement data clustering according to the improved agglomerative hierarchical clustering algorithm.Dividing similar data into the same cluster.Finally,the clustering results are compressed using the d-huffman coding algorithm.The purpose is to reduce the data transmission and reduce communication energy consumption.Finally,the algorithms in this thesis are compared with the existing algorithms.The results show that the algorithms in this thesis can further reduce the energy consumption of UAV system.