Trajectory Planning for Data Collection of Energy-Constrained Heterogeneous UAVs

Sensors

ZhengqinXu¹  Zhen Qin¹  Aijing Li¹  Hai Wang¹  Weihao Sun¹  Chao Dong²  Haipeng Dai³ 

[1] College of Communications Engineering, Army Engineering University of PLA, Nanjing 210042, China;College of Electronic and Information Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing 211106, China;Department of Computer Science and Technology, Nanjing University, Nanjing 210023, China;

关键词: unmanned aerial vehicles;    trajectory planning;    sensors;    data collection utility;   

DOI  :  10.3390/s19224884

来源: DOAJ

【 摘 要 】

Nowadays, Unmanned Aerial Vehicles (UAVs) have received growing popularity in the Internet-of-Things (IoT) which often deploys many sensors in a relatively wide region. Since the battery capacity is limited, sensors cannot transmit over a long distance. It is necessary for designing efficient sensor data collection mechanisms to prolong the lifetime of the IoT and enhance data collection efficiency. In this paper, we consider a UAV-enabled data collection scenario, where multiple heterogeneous UAVs with different energy constraints are employed to collect data from sensors. The value of data depends on the importance of the monitoring area of the sensor and the freshness of collected data. Our objective is to maximize the data collection utility by jointly optimizing the communication scheduling and trajectory of each UAV. The data collection utility is determined by the amount and value of the collected data. This problem is a variant of multiple knapsack problem, which is a classical NP-hard problem. First, we transform the initial problem into a submodular function maximization problem under energy constraints, and then we design a novel trajectory planning algorithm to maximize the data collection utility, while accounting for different values of data and different energy constraints of heterogeneous UAVs. Finally, under different network settings, the performance of the proposed trajectory planning algorithm is evaluated via extensive simulations. The results show that the proposed algorithm can obtain maximum data collection utility.

【 授权许可】

Unknown