Energy-Aware Dynamic Trajectory Planning for UAV-Enabled Data Collection in mMTC Networks

• Published in: IEEE transactions on green communications and networking Vol. 6; no. 4; pp. 1957 - 1971
• Main Authors: Shen, Lingfeng, Wang, Ning, Zhang, Di, Chen, Jun, Mu, Xiaomin, Wong, Kon Max
• Format: Journal Article
• Language: English
• Published: Piscataway IEEE 01.12.2022; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Algorithms; Altitude; Autonomous aerial vehicles; Clustering; Communication; Data collection; Data models; Dynamic trajectory planning; Energy consumption; Energy efficiency; Energy management; Hovering; massive machine-type communications; Particle swarm optimization; Task analysis; Trajectory; Trajectory planning; UAV-enabled data collection; Unmanned aerial vehicles
• ISSN: 2473-2400; 2473-2400
• DOI: 10.1109/TGCN.2022.3186841

A fundamental design problem for massive machine-type communication (mMTC) networks is efficient data collection from the machine-type communication devices (MTCDs), which is the subject of investigation in this paper. An unmanned aerial vehicle (UAV) being deployed to facilitate data collection from MTCDs is considered. Taking into account the limited energy for both the UAV and MTCDs, a problem of minimizing the total energy consumption subject to completion of the data collection tasks by planning the UAV trajectory is formulated. A Global Optimum (GOP) trajectory can be obtained for a UAV serving all the MTCDs simultaneously if the UAV's flying altitude is larger than <inline-formula> <tex-math notation="LaTeX">\sqrt {3} </tex-math></inline-formula> times its maximum service radius. However, communication energy efficiency drops as the UAV's altitude increases. Clustering-based service strategies and dynamic trajectory planning algorithms, namely clustered GOP (C-GOP) and clustered particle swarm optimization (C-PSO), are proposed to overcome the above issue. The data collection efficiency is maximized by locating the optimal UAV hovering point for each serving MTCD cluster, which is dynamically adjusted with the UAV hovering position until all MTCDs are served. It is shown that the GOP is the optimal strategy for a small number of MTCDs concentrated in a small area. While for large number of MTCDs or task area, the clustered algorithms are more favorable from energy efficiency, complexity and scalability perspectives.