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A Multi-Objective Hyper-Heuristic for Unmanned Aerial Vehicle Data Collection in Wireless Sensor Networks

机译:无线传感器网络中无人空中车辆数据收集的多目标超启发式

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Monitoring dangerous regions is one of the most important applications of wireless sensor networks. Limited by the danger of monitoring regions and the battery power of sensors, unmanned aerial vehicles (UAVs) are often used to collect data in such applications. How to properly schedule the movement of UAVs to efficiently collect data is still a challenging problem to be solved. In this paper, we formulate the UAV scheduling problem as a multi-objective optimization problem and design a genetic programming based hyper-heuristic framework to solve the problem. The simulation results show that our method can provide very promising performance in comparison with several state-of-the-art methods.
机译:监测危险区域是无线传感器网络最重要的应用之一。受监测区域的危险和传感器电池电量的限制,无人驾驶飞行器(无人机)通常用于收集这些应用中的数据。如何正确安排无人机的移动,有效地收集数据仍然是一个具有挑战性的问题。在本文中,我们将UAV调度问题作为一种多目标优化问题,并设计基于基于遗传编程的超启发式框架来解决问题。仿真结果表明,与多种最先进的方法相比,我们的方法可以提供非常有希望的性能。

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