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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机数据采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:监视危险区域是无线传感器网络最重要的应用之一。受监视区域和传感器电池电量危险的限制,无人驾驶飞机(UAV)通常在此类应用中用于收集数据。如何适当地安排无人机的运动以有效地收集数据仍然是有待解决的难题。在本文中,我们将无人机调度问题表述为一个多目标优化问题,并设计了一种基于遗传规划的超启发式框架来解决该问题。仿真结果表明,与几种最新方法相比,我们的方法可以提供非常有希望的性能。

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