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

doi:10.11959/j.issn.2096-3750.2022.00285

物联网学报 ›› 2022, Vol. 6 ›› Issue (3): 113-123.doi: 10.11959/j.issn.2096-3750.2022.00285

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¹, 陈健锋¹, 崔苗¹, 张广驰¹^,²

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

修回日期:2022-06-17 出版日期:2022-08-05 发布日期:2022-08-08
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
基金资助:
广东省科技计划项目(2020A050515010);广东省科技计划项目(2021A0505030015);广东特支计划项目(2019TQ05X409);澳门大学智慧城市物联网国家重点实验室开放课题(SKL-IoTSC(UM)-2021-2023/ORPF/A04/2022)

Trajectory and communication scheduling optimization for the rechargeable UAV aided data collection system

Qianwen LI¹, Jianfeng CHEN¹, Miao CUI¹, Guangchi ZHANG¹^,²

¹ School of Information Engineering, Guangdong University of Technology, Guangzhou 510006, China
² Guangdong Provincial Key Laboratory of Information Photonics Technology, Guangzhou 510006, China

Revised:2022-06-17 Online:2022-08-05 Published:2022-08-08
Supported by:
The Science and Technology Plan Project of Guangdong Province(2020A050515010);The Science and Technology Plan Project of Guangdong Province(2021A0505030015);The Special Support Plan for High-Level Talents of Guangdong Province(2019TQ05X409);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)

摘要/Abstract

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。

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

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.

Key words: rechargeable unmanned aerial vehicles, data collection, data lifetime, terminal scheduling, random optimization

中图分类号:

TN929.5

李茜雯, 陈健锋, 崔苗, 张广驰. 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, Guangchi ZHANG. 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.

图/表 8

图1

表1

仿真环境参数设置"

变量	取值	变量	取值
B	2 MHz	$σ^{2}$	-110 dBm
H	100 m	$ρ_{0}$	-60 dB
R^th	15 Mbit/s	P^r	100 W
F^max	100 000 J	V ^max	25 m/s
$K$	20～80	D	1 Gbit/s
$η$	4.2	P^h	165 W
$[τ_{1}, τ_{2}]$	[0.9,1.6]×10^-4	$[ψ_{1}, ψ_{2}]$	[9.3,16.6]×10^-3
$[τ_{3}, τ_{4}]$	[1.8,3.6]	$[ψ_{3}, ψ_{4}]$	[79.9,357.2]
T ^max	900～3 000 s	终端s	20～90

表1

表2

图2

图3

表3

图4

图5

参考文献 17

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请求服务终端数目/个	30		50		80
数据生命期/s	1 800	2 000	1 800	2 000	1 800	2 000
本文算法的通信数据量/(Gbit·s^-1)	10.673	11.493	10.827	12.575	11.969	12.938
“基于距离”算法的通信数据量/(Gbit·s^-1)	13.766	12.141	10.435	12.575	11.068	12.864
“基于数据量”算法的通信数据量/(Gbit·s^-1)	13.919	13.136	11.124	12.872	12.873	13.16

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

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图/表 8

参考文献 17

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请求服务终端数量/个	50	60	70	80	90	均值	方差
随机优选	15	15	16	16	16	15.6	0.24
网格搜索	14	15	15	16	15	15	0.4
网格迭代	14	11	14	14	15	13.6	1.84

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[2]	李政,张圣,张卢喻,张杨. 基于物联网技术的桥梁监测系统[J]. 物联网学报, 2018, 2(3): 104-110.