Joint Clustering and 3-D UAV Deployment for Delay-Aware UAV-Enabled MTC Data Collection Networks

IF 2.2 Q3 ENGINEERING, ELECTRICAL & ELECTRONIC
Lingfeng Shen;Huanran Zhang;Ning Wang;Ying Cui;Xiang Cheng;Xiaomin Mu
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引用次数: 0

Abstract

The design of timely data collection for a machine-type communication (MTC) network by unmanned-aerial-vehicle (UAV) platform is investigated. The ground-based MTC devices are clustered for efficient service, and the UAV station's deployment in the 3-D space is optimized. The corresponding mission time minimization problem is formulated as a coupled mixed-integer nonlinear program. For tractability, the original problem is decomposed into two subproblems respectively dealing with clustering-hovering optimization and intercluster UAV traveling path minimization. An alternating clustering-hovering optimization (ACH) and ant colony optimization (ACO) solution approach is proposed accordingly. Simulations are conducted to validate the superiority of the proposed ACH–ACO scheme over the scheme based on $k$ -means clustering.
为支持延迟感知的无人机 MTC 数据采集网络提供联合集群和三维无人机部署功能
研究了无人机(UAV)平台为机器型通信(MTC)网络及时收集数据的设计。为了提供高效服务,对地面 MTC 设备进行了集群,并优化了无人机站在三维空间中的部署。相应的任务时间最小化问题被表述为一个耦合混合整数非线性程序。为了便于理解,原问题被分解成两个子问题,分别处理集群徘徊优化和集群间无人机飞行路径最小化。相应地,提出了一种交替聚类徘徊优化(ACH)和蚁群优化(ACO)的解决方法。通过仿真验证了所提出的 ACH-ACO 方案优于基于 $k$-means 聚类的方案。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
IEEE Sensors Letters
IEEE Sensors Letters Engineering-Electrical and Electronic Engineering
CiteScore
3.50
自引率
7.10%
发文量
194
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