Research On Path Planning Algorithm For UAV-Assisted Internet Of Things Data Acquisition

The Internet of Things and wireless sensor networks play a crucial role in modern smart technologies.Iot devices and applications are used to process data extracted from wireless sensor devices and transmit it to remote locations.In this application scenario,the collected data needs to be delivered to the data center as soon as possible for online data analysis and decision-making.Drones,due to their mobility and flexibility,are widely seen as a promising method to assist iot devices in collecting data.Generally,during dynamic data collection,not enough drones are deployed to cover all targets at once,so drones need a pre-set flight path.This paper mainly aims at the path planning problem when drones assist iot data.In the area of a large number of sensor nodes with certain coordinates,drones are adopted as the data acquisition platform of ground iot devices.Based on the optimization of UAV deployment,the flight path of UAV is planned by heuristic algorithm to minimize the energy consumption of the system.The main research contents of this paper are as follows:(1)Uav path planning modeling:Formalize the network model and performance constraints in the process of data collection by establishing the system model of UAV-assisted data acquisition of Internet of Things devices,and obtain the position and number deployment of UAV stopping points through DEVIPS algorithm.According to the mapping between UAV path planning model and traveling salesman problem model,The ant colony algorithm is used to establish a suitable UAV path planning model,and the feasibility of the algorithm is proved by simulation experiments.(2)Unequal distribution of initial pheromones:In order to make the algorithm have better search performance in the initial stage,the blind search of early planning is avoided.Firstly,greedy algorithm is used to find a suboptimal path in the initial stage,and then max-min ant colony algorithm is used to carry out unequal initial pheromone allocation between the suboptimal path and other paths to reduce the probability of the poor path being selected,so that the algorithm has good search performance in the initial stage.(3)Pheromone smoothing mechanism:In order to reduce the possibility of the algorithm falling into local optimal,the difference information of two adjacent iteration pheromone matrices was compared to determine whether the algorithm was close to stagnation.Then,a new pheromone smoothing mechanism is introduced to narrow the gap between pheromone concentrations,so as to jump out of the local optimal,improve the global search ability of the algorithm,and to a certain extent improve the quality of the algorithm in the stagnation period.In order to verify the effectiveness of the improved algorithm,simulation experiments are carried out compared with the basic ant colony algorithm.Simulation results show that the improved algorithm is effective.On the basis of minimizing the energy consumption of UAV-assisted data acquisition in iot,the goal of fast path planning is realized.