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

Massive machine-type communications (mMTC) is a key enabling technology for Internet of Things (IoT) services in 5G and beyond. Efficient data collection from massive machine-type communication devices (MTCDs) performing sensing tasks is an important part of the service. In this paper, we consider an unmanned aerial vehicle (UAV) being deployed to facilitate data collection from MTCDs. Taking into account the limited energy of battery-powered MTCDs, the UAV trajectory is optimized to improve the energy efficiency of data collection. By fixing the starting and ending points of the UAV trajectory, a globally optimal (GO) trajectory can be obtained, based on the assumption that the UAV’s serving radius and access capacity (number of served MTCDs) are unlimited. Interestingly, it is shown that the optimal trajectory always exists as long as the UAV flying height is greater than its service radius multiplied by a constant. However, the increase of the UAV flying height deteriorates the channel, leading to reduced efficiency of energy consumption. Alternatively, a greedy dynamic (GD) trajectory optimization scheme with limited UAV service radius and access capacity is then investigated, resulting in the optimal service location of the UAV being at a lower flying height, and the energy consumption for accomplishing the data collection task being reduced. Specifically, the UAV sorts the MTCDs within its serving radius based on the distance and selects its closest serving MTCD set. In a serving MTCD set, there is an optimal hovering location that maximizes the data collection efficiency. The UAV dynamically adjusts its service set and the optimal data collection location when MTCDs finish their data transmission and exit the service set. The process continues until all the MTCDs are served and the UAV arrives at the ending point of the trajectory. Simulation results show that both the GO and GD algorithm can improve the efficiency of overall energy consumption. In particular, the online dynamic trajectory optimization scheme is less restrictive and achieves higher efficiency.