Research On Path Planning Under The Security Capacity Guarantee Of UAV Sensor Network

As a new monitoring method,wireless sensor network can monitor the surrounding environment and collect data.It is an important part of the Internet of Things and has been widely used in various fields.Since UAVs are easy to deploy,highly flexible,and highly maneuverable,using UAVs to collect sensor monitoring data can reduce latency and enhance the sustainability of wireless sensor networks.However,the broadcast nature of wireless channels increases the risk of eavesdropping on wireless communications.In order to prevent the leakage of monitoring data and sensitive information,the security of wireless communication must be guaranteed.However,the existing work does not consider the requirement of service quality while protecting the security of wireless communication,which will lead to too short communication time of some nodes and thus cause interruption and loss of data transmission.This thesis studies the wireless communication security problem of using UAVs for data collection in wireless sensor networks.Specifically,the sensors are distributed in a certain range to monitor the environmental data within the range,eavesdroppers are scattered around it to wait for the opportunity to conduct illegal eavesdropping,and the drones regularly cruise in this area to collect the data sensed by the sensors.Among them,UAVs have mobility constraints to express their energy constraints;systems have data collection constraints to meet the requirements of quality of service;sensors have transmission power constraints to extend the life of the network.Based on this,this thesis proposes an average secrecy rate maximization problem.Aiming at the above optimization problem,this thesis solves the non-smooth problem by transforming it equivalently.For the transformed problem,an iterative optimization algorithm is proposed in this paper based on the block coordinate descent method.Among them,the algorithm consists of three parts: sensor transmission power optimization,UAV trajectory optimization and data collection optimization.In the transmission power optimization,the sensor timely adjusts its own transmission power according to the position of the drone and the eavesdropper,which can prolong the life of the wireless sensor network and improve the transmission rate of the legal channel.In the UAV trajectory optimization,the design of the UAV path can enhance the legal link strength while degrading the eavesdropping link strength,so as to improve the secrecy rate.In the optimization of data collection,by planning the scheduling sequence and communication time of UAV data collection,it can meet the requirements of service quality and improve the secrecy rate.Through analysis,the algorithm proposed in this thesis can guarantee fast convergence and has polynomial time complexity.To test the performance of the proposed algorithm,three comparison algorithms are designed in this thesis.Among them,TPA-WTP algorithm does not optimize the trajectory of the UAV,TPA-WPC algorithm does not optimize the transmission power,and TPA-WTC algorithm does not optimize the scheduling and data collection.In this thesis,the proposed algorithm and three comparison algorithms are compared and simulated.The experimental results show that the iterative optimization algorithm can converge after about 5 iterations.In terms of improving the average secrecy rate,UAV trajectory optimization is more effective when the average power is small,and power control is more effective when the average power is large.Compared with TPA-WTP algorithm and TPA-WPC algorithm,the algorithm in this thesis improves the average secrecy rate by 15.7%and 159.8%,respectively.Compared with the TPA-WTC algorithm,when the UAV flight period is greater than 70 s,the proposed algorithm is 30.7% and 20.9% higher on average than the TPA-WTC algorithm in the two different task distributions.