School of Computer Science and Engineering, Southeast University, Nanjing 211189, China.
Sensors (Basel). 2018 Sep 2;18(9):2913. doi: 10.3390/s18092913.
We consider an Unmanned Aerial Vehicle (UAV, also known as drone) as an aerial sink to travel along a natural landscape or rural industrial linear infrastructure to collect data from deployed sensors. We study a joint schedule problem that involves flight planning for the drone and transmission scheduling for sensors, such that the maximum amount of data can be collected with a limited individual energy budget for the UAV and the sensors, respectively. On one hand, the flight planning decides the flight speed and flight path based on sensor locations, energy budgets, and the transmission schedule. On the other hand, the transmission schedule decides for each sensor when to deliver data and what transmission power to use based on the energy budgets and flight plan. By observing three import optimality properties, we decouple the joint problem into two subproblems: drone flight planning and sensor transmission scheduling. For the first problem, we propose a dynamic programming algorithm to produce the optimal flight planning. For the second problem, with a flight plan as input, we introduce a novel technique (), which together with dynamic programming, is the key to achieve an optimal transmission schedule that maximizes data collection. Simulations show that the separately determined flight plan and transmission schedule are near-optimal for the original joint problem.
我们将无人飞行器 (UAV，也称为无人机) 视为空中接收器，沿着自然景观或农村工业线性基础设施飞行，从部署的传感器中收集数据。我们研究了一个联合调度问题，该问题涉及无人机的飞行规划和传感器的传输调度，以使无人机和传感器各自的有限能量预算能够收集最大量的数据。一方面，飞行规划根据传感器位置、能量预算和传输计划来决定飞行速度和飞行路径。另一方面，传输计划根据能量预算和飞行计划决定每个传感器何时传输数据以及使用多大的传输功率。通过观察三个重要的最优性性质，我们将联合问题解耦为两个子问题：无人机飞行规划和传感器传输调度。对于第一个问题，我们提出了一种动态规划算法来生成最优的飞行规划。对于第二个问题，我们引入了一种新的技术（），它与动态规划一起，是实现最大化数据收集的最优传输计划的关键。仿真结果表明，分别确定的飞行计划和传输计划对于原始联合问题是接近最优的。
