Energy-Constrained Completion Time Minimization in UAV-Enabled Internet of Things

被引:81
作者
Gu, Jiangchun [1 ]
Wang, Haichao [1 ]
Ding, Guoru [1 ]
Xu, Yitao [1 ]
Xue, Zhen [1 ]
Zhou, Huaji [2 ]
机构
[1] Army Engn Univ PLA, Coll Commun Engn, Nanjing 210007, Peoples R China
[2] Sci & Technol Commun Informat Secur Control Lab, Jiaxing 314000, Peoples R China
基金
中国国家自然科学基金;
关键词
Optimization; Trajectory; Internet of Things; Data dissemination; Minimization; Wireless communication; Sensors; Alternating descent method; data dissemination; Internet of Things (IoT); trajectory optimization; transmit power optimization; unmanned aerial vehicles (UAVs); TRAJECTORY DESIGN; COGNITIVE INTERNET; MAXIMIZATION; COMMUNICATION; CONSUMPTION; NETWORKS; PARADIGM; ALTITUDE;
D O I
10.1109/JIOT.2020.2981092
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
Unmanned-aerial-vehicles (UAVs)-enabled wireless communication for Internet-of-Things (IoT) applications has attracted increasing attention. This article studies a UAV-assisted data dissemination system, where a rotary-wing UAV is dispatched to disseminate data to terrestrial IoT devices. We target to minimize the completion time via a joint optimization of the UAV trajectory and transmit power, while considering the indispensable constraints which cover the maximum energy budget, speed, transmit power of the UAV, and data requirement for each IoT device. First, we formulate the UAV data dissemination as a completion time minimization problem. To tackle the nonconvex optimization problem, the original problem is transformed into two subproblems: 1) the trajectory optimization and 2) the transmit power optimization, respectively, by introducing auxiliary variables and leveraging the concave-convex procedure. Then, we develop a joint trajectory and transmit power algorithm via tailoring the successive convex approximation and alternating descent method. We further improve the algorithm by maximizing the throughput instead of minimizing the completion time in the transmit power optimization process. The improved algorithm not only reduces the computational complexity but also enhances the achieved performance. In addition, simulation results demonstrate the superior performance of the proposed algorithms under various parameter configurations.
引用
收藏
页码:5491 / 5503
页数:13
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