可充电无人机辅助数据采集系统的飞行路线与通信调度优化

李茜雯; 陈健锋; 崔苗; 张广驰

doi:10.11959/j.issn.2096-3750.2022.00285

您当前的位置：

首页 >

文章列表页 >

可充电无人机辅助数据采集系统的飞行路线与通信调度优化

理论与技术 | 更新时间：2024-06-05

- 可充电无人机辅助数据采集系统的飞行路线与通信调度优化
- Trajectory and communication scheduling optimization for the rechargeable UAV aided data collection system
- 物联网学报 2022年6卷第3期页码：113-123
- 作者机构：
  
  1. 广东工业大学信息工程学院，广东广州 510006
  2. 广东省信息光子技术重点实验室，广东广州 510006
- 作者简介：
  
  [ "李茜雯（1997- ），女，广东工业大学信息工程学院硕士生，主要研究方向为新一代无线通信技术、无人机等" ]
  [ "陈健锋（1998- ），男，广东工业大学信息工程学院硕士生，主要研究方向为新一代无线通信技术、智能反射面等" ]
  [ "崔苗（1978- ），女，广东工业大学信息工程学院讲师，主要研究方向为新一代无线通信技术等" ]
  [ "张广驰（1982- ），男，广东工业大学信息工程学院教授，主要研究方向为新一代无线通信技术等" ]
- 基金信息：
  
  广东省科技计划项目;The Science and Technology Plan Project of Guangdong Province(2020A050515010);广东省科技计划项目;The Science and Technology Plan Project of Guangdong Province(2021A0505030015);广东特支计划项目;The Special Support Plan for High-Level Talents of Guangdong Province(2019TQ05X409);澳门大学智慧城市物联网国家重点实验室开放课题;The Open Research Project Programme of the State Key Laboratory of Internet of Things for Smart City (University of Macau)(SKL-IoTSC(UM)-2021-2023/ORPF/A04/2022)
- DOI：10.11959/j.issn.2096-3750.2022.00285
  中图分类号： TN929.5
- 纸质出版日期：2022-09-30，
  
  网络出版日期：2022-09，
- 稿件说明：
移动端阅览
李茜雯, 陈健锋, 崔苗, 等. 可充电无人机辅助数据采集系统的飞行路线与通信调度优化[J]. 物联网学报, 2022,6(3):113-123.

QIANWEN LI, JIANFENG CHEN, MIAO CUI, et al. Trajectory and communication scheduling optimization for the rechargeable UAV aided data collection system. [J]. Chinese journal on internet of things, 2022, 6(3): 113-123.
李茜雯, 陈健锋, 崔苗, 等. 可充电无人机辅助数据采集系统的飞行路线与通信调度优化[J]. 物联网学报, 2022,6(3):113-123. DOI： 10.11959/j.issn.2096-3750.2022.00285.

QIANWEN LI, JIANFENG CHEN, MIAO CUI, et al. Trajectory and communication scheduling optimization for the rechargeable UAV aided data collection system. [J]. Chinese journal on internet of things, 2022, 6(3): 113-123. DOI： 10.11959/j.issn.2096-3750.2022.00285.

摘要

考虑一个可充电无人机辅助的无线传感器网络，网络包含多个地面终端，每个终端需要传输大量具有时间敏感性的数据。由于电池容量有限，无人机无法通过单次飞行任务采集全部终端的数据，它需要多次返回充电桩补充能量。研究了无人机的终端调度、飞行轨迹、飞行速度与传输速率优化，在数据生命期内最大化采集的终端数量。变量之间高度耦合且存在离散的二进制变量，涉及的优化问题难以求解，故而提出基于随机优化和特征工程思想的算法求解该优化问题。首先，引入飞行—悬停通信协议降低轨迹优化的复杂度，然后创新性地提出基于影响因子和随机优选的通信调度算法。该算法通过提取终端上影响无人机服务时间的特征赋予终端优先级，计算出不同终端服务总数下的最优调度方案，从而把优化问题简化成多个求解最短耗时的子问题，并利用块坐标下降法和连续凸近似技术求得子问题的解。仿真结果表明，与几种基准策略相比，所提优化算法在不同数据生命期与不同请求服务终端总数的场景下都有显著的性能优势。

Abstract

A rechargeable unmanned aerial vehicle (UAV) aided wireless sensor network was considered

which consists of multiple ground terminals with a large amount of time-sensitive data to be collected.Due to the limited battery capacity

the UAV cannot collect the data from all terminals through a single flight mission

and it needs to return to the charging pile to replenish its flight energy several times during the whole mission.The optimization of the terminal scheduling

trajectory

flight speed and transmission rate for the UAV was studied to maximize the number of terminals whose data had been collected within the data lifetime limit.Due to the variable coupling and the existence of discrete binary scheduling variables

the considered optimization problem is difficult to solve.To tackle such a difficulty

an efficient algorithm was proposed based on the stochastic optimization and the feature engineering.Specifically

the flight hover communication protocol was introduced to simplify the UAV flight process.And then a terminal scheduling algorithm was innovatively proposed with the influence factor and the stochastic preference

which extracted the features that affect the service time of the UAV

optimized the weights of the features

and further simplified the optimization problem into multiple subproblems.The subproblems were then solved by using the block coordinate descent and successive convex approximation techniques.Simulation results show that the proposed optimization algorithm achieves significant performance gains over several benchmark schemes in the scenarios with different data lifetime requirements and different numbers of ground terminals.

关键词

可充电无人机数据采集数据生命期终端调度随机优化

Keywords

rechargeable unmanned aerial vehiclesdata collectiondata lifetimeterminal schedulingrandom optimization

references

ZENG Y, WU Q Q, ZHANG R . Accessing from the sky,a tutorial on UAV communications for 5G and beyond[J]. Proceedings of the IEEE, 2019,107(12): 2327-2375.

MOZAFFARI M, SAAD W, BENNIS M ,et al. Unmanned aerial vehicle with underlaid device-to-device communications,performance and tradeoffs[J]. IEEE Transactions on Wireless Communications, 2016,15(6): 3949-3963.

SHARMA V, SRINIVASAN K, CHAO H C ,et al. Intelligent deployment of UAVs in 5G heterogeneous communication environment for improved coverage[J]. Journal of Network and Computer Applications, 2017,85: 94-105.

NAQVI S A R, HASSAN S A, PERVAIZ H ,et al. Drone-aided communication as a key enabler for 5G and resilient public safety networks[J]. IEEE Communications Magazine, 2018,56(1): 36-42.

FAN W L, WU Y, JU S H ,et al. Secure UAV communication with robust communication and trajectory design[C]// Proceedings of 2019 International Conference on Computer,Information and Telecommunication Systems (CITS). Piscataway,IEEE Press, 2019: 1-5.

AL-HOURANI A, KANDEEPAN S, JAMALIPOUR A . Modeling air-to-ground path loss for low altitude platforms in urban environments[C]// Proceedings of 2014 IEEE Global Communications Conference. Piscataway,IEEE Press, 2014: 2898-2904.

MU C X, ZHANG Y . Learning-based robust tracking control of quadrotor with time-varying and coupling uncertainties[J]. IEEE Transactions on Neural Networks and Learning Systems, 2020,31(1): 259-273.

ZHAN C, ZENG Y, ZHANG R . Energy-efficient data collection in UAV enabled wireless sensor network[J]. IEEE Wireless Communications Letters, 2018,7(3): 328-331.

YANG D C, WU Q Q, ZENG Y ,et al. Energy tradeoff in ground-to-UAV communication via trajectory design[J]. IEEE Transactions on Vehicular Technology, 2018,67(7): 6721-6726.

GHDIRI O, JAAFAR W, ALFATTANI S ,et al. Energy-efficient multi-UAV data collection for IoT networks with time deadlines[C]// Proceedings of GLOBECOM 2020 - 2020 IEEE Global Communications Conference. Piscataway,IEEE Press, 2020: 1-6.

ZENG Y, XU X L, ZHANG R . Trajectory design for completion time minimization in UAV-enabled multicasting[J]. IEEE Transactions on Wireless Communications, 2018,17(4): 2233-2246.

FAN J Y, CUI M, ZHANG G C ,et al. Throughput improvement for multi-hop UAV relaying[J]. IEEE Access, 7: 147732-147742.

LI J X, ZHAO H T, WANG H J ,et al. Joint optimization on trajectory,altitude,velocity,and link scheduling for minimum mission time in UAV-aided data collection[J]. IEEE Internet of Things Journal, 2020,7(2): 1464-1475.

ZHAN C, HU H, SUI X F ,et al. Completion time and energy optimization in the UAV-enabled mobile-edge computing system[J]. IEEE Internet of Things Journal, 2020,7(8): 7808-7822.

YAO J J, ANSARI N . QoS-aware rechargeable UAV trajectory optimization for sensing service[C]// Proceedings of ICC 2019 - 2019 IEEE International Conference on Communications. Piscataway,IEEE Press, 2019: 1-6.

YAO J J, ANSARI N . QoS-aware power control in Internet of drones for data collection service[J]. IEEE Transactions on Vehicular Technology, 2019,68(7): 6649-6656.

GOODFELLOW I, BENGIO Y, COURVILLE A . Deep learning[M]. Cambridge,MA,USA,MIT press, 2016.

浏览量

695

下载量

CSCD

文章被引用时，请邮件提醒。

提交

工具集

关联资源

基于深度强化学习的无人机数据采集和路径规划研究

基于物联网技术的桥梁监测系统