UAV-Enabled Data Collection Over Clustered Machine-Type Communication Networks: AEM Modeling and Trajectory Planning

• Published in: IEEE transactions on vehicular technology Vol. 71; no. 9; pp. 10016 - 10032
• Main Authors: Shen, Lingfeng, Wang, Ning, Zhu, Zhengyu, Xu, Wei, Li, Yue, Mu, Xiaomin, Cai, Lin
• Format: Journal Article
• Language: English
• Published: New York IEEE 01.09.2022; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: 5G mobile communication; Algorithms; Artificial energy map; Autonomous aerial vehicles; clustered MTC network; Clustering; Clustering algorithms; Communication networks; Data collection; Energy consumption; Energy efficiency; Greedy algorithms; Hovering; Internet of Things; Machine learning; Modelling; Optimization; Optimization techniques; Strategy; Trajectory; Trajectory planning; UAV-enabled data collection; Unmanned aerial vehicles
• ISSN: 0018-9545; 1939-9359
• DOI: 10.1109/TVT.2022.3181158

Machine-type communications (MTC) is a key technology for Internet-of-Things (IoT) services in 5G mobile communications and beyond. An essential design problem for an MTC network is the efficient and scalable data collection from low-power machine-type communication devices (MTCDs). This paper uses unmanned aerial vehicles (UAVs) to facilitate data collection from a clustered MTC network on the ground. The notion of artificial energy map (AEM) is introduced as a novel modeling technique for energy efficiency analysis, which is critical to the subject of investigation here considering the limited energy of battery-powered MTCDs and UAVs. The proposed design framework first determines the number of MTCD clusters <inline-formula><tex-math notation="LaTeX">L_\mathrm{{opt}}</tex-math></inline-formula> according to a certain criterion. A greedy learning clustering (GLC) algorithm is then employed to divide the MTCDs into <inline-formula><tex-math notation="LaTeX">L_\mathrm{{opt}}</tex-math></inline-formula> clusters. For each MTCD cluster, an AEM is constructed, and the optimal UAV hovering strategy within the cluster can be obtained accordingly. Finally, the UAV stations travel across the clusters and collect data from each cluster while hovering above it. This AEM-based modeling technique leads to a solution that can effectively improve the energy efficiency (EE) of UAV-enabled data collection. However, the MTCD clustering strategy, UAV hovering strategy, and UAV flying strategy all have impacts on the overall energy efficiency, which results in a coupled optimization problem that is difficult to solve. The GLC-AEM method is proposed to decouple the original EE optimization problem into sub-problems that can be handled easily by standard optimization techniques. Simulation results show that the GLC-AEM algorithm can be applied to UAV-enabled data collection scenarios with single and multiple UAV stations, and it can improve the overall EE effectively. Besides, the GLC-AEM algorithm shows good scalability and consistent performance in clustered MTC networks. The more MTCDs, the higher the achieved EE.