Energy-Efficient UAV Trajectory Planning for Data Collection and Computation in mMTC Networks

As one of the three main scenarios of 5G, massive machine-type communications (mMTC) is expected to play an essential role within future 5G system. Unmanned aerial vehicles (UAVs) also attracts more attentions these years. However, the limited battery of both MTC devices (MTCDs) and UAVs constrains the deployment. Besides, the limited computation capacity of MTCDs also impacts the network optimization in mMTC networks. In this paper, UAV-based aerial base station is deployed to collect data and provide computing service for MTCDs. We introduce a non-service-tolerant parameter and propose a hybrid hovering positions selection (HHPS) algorithm. The hovering positions of UAV with the minimum power consumption of MTCDs are selected. Furthermore, We propose a trajectory planning method based on Cuckoo Search (CS) algorithm. The energy consumption and the throughput of UAV, the latency of MTCD tasks and collecting and computing efficiency with different priorities are optimized. Simulations are carried out to illustrate that the proposed HHPS algorithm and trajectory planning algorithm have superior performances compared with existing works.