Research On UAV-assisted IoT Data Collection Path Planning Algorithm

In recent years,unmanned aerial vehicles(UAVs)have gradually entered people’s vision and made significant progress in various fields.Compared with traditional ground communication,UAV has the advantages of strong mobility,on-demand deployment,flexible configuration,etc.It is often used to assist wireless sensor networks in data collection and improve network performance.This paper focuses on the path planning of data collection in wireless sensor networks assisted by UAVs.In the first part of this article,an improved differential evolution algorithm based on genetic perturbations(Gaussian perturbations)is proposed to address the problem of slow convergence speed and susceptibility to local optima in path planning using the standard differential evolution algorithm.This algorithm aims to improve the optimization performance of the algorithm.Then the energy consumption of a wireless sensor network is analyzed,and the energy consumption expression of ground air data transmission between sensor network and UAV is derived.Under the constraint of data transmission rate,the energy consumption of the sensor network is minimized by jointly planning the drone flight trajectory and sensor node power.The optimization problem is expressed as a non convex form with constraints.The block Coordinate descent and the improved differential evolution algorithm proposed in this paper are used to solve the original problem to obtain an approximate optimal solution.Finally,algorithm simulation was conducted,and the simulation results showed that the data collection strategy proposed in this paper for jointly planning the trajectory of unmanned aerial vehicles and transmitting power of sensor nodes can effectively reduce the energy consumption of ground sensor networks compared to other benchmark collection strategies;The improved differential evolution algorithm based on gene perturbation proposed in this article has significantly improved convergence performance compared to the standard differential evolution algorithm and PSO particle swarm optimization algorithm.The second part of this article focuses on the path planning goal of unmanned aerial vehicles(UAVs)that cannot achieve real-time obstacle avoidance while also considering data collection efficiency when there are unknown obstacles on the ground.Based on this situation,this article first improves the cost function and search strategy of the standard A* algorithm to improve the algorithm’s search efficiency and path security.Secondly,throughput guidance is introduced into the evaluation function of the standard DWA algorithm to improve the efficiency of drone data collection.Finally,the improved A* and improved DWA algorithms are fused to plan a drone trajectory that can avoid obstacles in real time while also taking into account the efficiency of data collection for ground sensor networks.Through simulation,the improved fusion algorithm proposed in this article can significantly improve the data collection efficiency of unmanned aerial vehicles while avoiding obstacles in real-time.