Unmanned aerial vehicle trajectory optimization method and system in Internet of Things data collection

Abstract

The invention discloses an unmanned aerial vehicle trajectory optimization method and system in Internet of Things data collection, and the method comprises the steps: firstly constructing a deep reinforcement learning framework network, then inputting state information including pheromone concentration into a strategy network to obtain the motion of an unmanned aerial vehicle, and repeating this process; using the pheromone concentration to calculate an instantaneous reward value and obtain an accumulated reward value; and determining whether optimization training is completed or not through the state of the accumulated reward value, and finally obtaining an unmanned aerial vehicle optimization trajectory according to the trained strategy network. According to the method, channel modeling is performed on the unmanned aerial vehicle and the Internet of Things nodes in a simulation environment, the existence of a line-of-sight (LoS) link and a non-line-of-sight (NLoS) link is considered during channel modeling, and the unmanned aerial vehicle can quickly complete Internet of Things data collection on the premise of reflecting the dynamic change of the actual Internet of Things communication environment. A data acquisition task is executed according to the optimized unmanned aerial vehicle trajectory, the actual flight requirement of the unmanned aerial vehicle can be better met, and the data acquisition task can be more accurately completed.

Description

technical field [0001] The invention relates to the technical field of unmanned aerial vehicle wireless communication, in particular to a method and system for optimizing trajectory of an unmanned aerial vehicle in data collection of the Internet of Things. Background technique [0002] UAVs have flexible mobility, and can approach potential IoT nodes and collect data through trajectory optimization with a low-power connection scheme. Therefore, UAV communication technology is expected to play a key role in the next generation of wireless communication systems, providing wider and deeper coverage and connections for the growing mass of wireless terminals. Compared with the IoT system based on the ground base station, the air base station system based on the UAV has remarkable characteristics, such as improving the line-of-sight channel probability, improving spectrum and energy efficiency, etc. [0003] In the existing UAV trajectory optimization training, the first thing i...

AI Technical Summary

Problems solved by technology

In addition, in the early stage of training, the strategy executed by the UAV is random, and reward acquisition requires a series of complex operations

Therefore, the data collection task of UAV is a sparse reward problem, which will cause the reinforcement learning algorithm to progress slowly in the iterative process, and even difficult to converge

Images