Optimization algorithm for data collection and dissemination with age of information minimization in multi-uav-assisted iot

WANG Zimeng; ZHU Qi

doi:10.11959/j.issn.1000-0801.DXKX260155

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Optimization algorithm for data collection and dissemination with age of information minimization in multi-uav-assisted iot

更新时间：2026-06-23

- Optimization algorithm for data collection and dissemination with age of information minimization in multi-uav-assisted iot
- Telecommunications Science (2026)
- 作者机构：
  
  1.南京邮电大学江苏省无线通信重点实验室，江苏南京210003
  2.南京邮电大学教育部泛在网络健康服务系统工程研究中心，江苏南京210003
- 作者简介：
- 基金信息：
  
  National Natural Science Foundation of China(92367302);Jiangsu Provincial Key and Development Program(BE2022068-2)
- DOI：10.11959/j.issn.1000-0801.DXKX260155
  CLC： TN929.5
- Received：09 March 2026，
  
  Revised：2026-04-28，
  
  Accepted：23 June 2026，
- 稿件说明：
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WANG Zimeng, ZHU Qi. Optimization algorithm for data collection and dissemination with age of information minimization in multi-uav-assisted iot[J/OL]. Telecommunications Science, 2026.
DOI：

WANG Zimeng, ZHU Qi. Optimization algorithm for data collection and dissemination with age of information minimization in multi-uav-assisted iot[J/OL]. Telecommunications Science, 2026. DOI： 10.11959/j.issn.1000-0801.DXKX260155.

摘要

随着物联网技术的普及，海量传感器数据需要被高效处理，无人机凭借其灵活部署与移动通信的优势，突破了固定设施的局限，实现了对目标区域数据的快速采集与分发。针对无人机辅助物联网中数据采集与分发的信息新鲜度问题，本文提出了一种基于信息年龄的无人机数据采集和分发的优化算法。在无线传感器节点的通信距离、无人机数量和信噪比阈值的限制下，建立了用户获得数据的平均AoI最小化优化问题。将该问题分解为成簇数目、区域划分以及无人机轨迹三个优化子问题，采用改进的K-means聚类算法对区域内的传感器节点进行成簇，然后根据簇首的位置采用基于几何质心距离的区域划分算法将整个区域分成多个子区域，每个无人机负责采集一个子区域内的数据，采用基于固定起点和终点的2-opt优化算法获得每个无人机的飞行轨迹。然后选择一架能量最优的无人机，其他无人机以频分多址的方式与其进行数据共享，再由该无人机以飞行和悬停混合方式将数据分发给用户，通过预测飞行时可以分发的数据量以获得无人机在关联用户上方的停留时间。仿真结果表明，本文提出的优化算法可以有效减少平均AoI，有效保障数据的新鲜度。

Abstract

With the widespread adoption of Internet of Things (IoT) technology

massive amounts of sensor data

require efficient processing. Unmanned Aerial Vehicles (UAVs)

leveraging their flexible deployment and mobile communication advantages

break through the limitations of fixed infrastructure

enabling rapid data collection and distribution in target areas. To address the issue of information freshness in UAV-assisted IoT data collection and distribution

this paper proposes an optimization algorithm for UAV data collection and distribution based on Information Age. Under the constraints of wireless sensor node communication range

UAV quantity

and signal-to-noise ratio threshold

an optimization problem is formulated to minimize the average Age of Information (AoI) for users receiving data. This problem is decomposed into three sub-problems: cluster number optimization

area partitioning

and UAV trajectory planning. An improved K-means clustering algorithm is employed to cluster the sensor nodes within the area. Then

based on the positions of the cluster heads

a region partitioning algorithm using geometric centroid distance is applied to divide the entire area into multiple sub-regions

with each UAV responsible for collecting data from one sub-region. A 2-opt optimization algorithm with fixed start and end points is utilized to obtain the flight trajectory for each UAV. Subsequently

the UAV with optimal energy efficiency is selected. Other UAVs share data with it using Frequency Division Multiple Access (FDMA). This selected UAV then distributes the data to users through a hybrid strategy of flying and hovering

predicting the amount of data that can be distributed during flight to determine the hovering time above associated users. Simulation results demonstrate that the proposed optimization algorithm effectively reduces the average AoI

thereby ensuring data freshness.

关键词

Keywords

references

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