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Trajectory Optimization of UAV for Efficient Data Collection from Wireless Sensor Networks

机译:从无线传感器网络高效采集数据的无人机航迹优化

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Unmanned Aerial Vehicles (UAVs) are expected to be an important component in the upcoming wireless communication field, which are increasingly used as data collectors to gather sensing data from Wireless Sensor Networks (WSNs) due to their high mobility. Since the storage capacity and lifetime of sensors are increasing with the development of science and technology, sensors can store more and more sensing data about the monitoring area. However, due to the energy limitation of UAVs, we can not collect all data from WSN in limited time. Therefore, in this paper, we investigate the Maximizing Data Collection Proportion (MDCP) problem: given the limited budget of UAV, the objective is to find the trajectory of UAV such that the minimum data collection proportion of collected data to the stored data among all sensors is maximized. We first prove that the MDCP problem is NP-hard. Then we propose two approximation algorithms to design the trajectory of UAV, and give the theoretical analysis for the algorithms. Finally, we present numerical results in different scenarios to evaluate the effectiveness of the proposed algorithms.
机译:无人机有望成为即将到来的无线通信领域的重要组成部分,由于它们的高移动性,越来越多的无人机被用作数据收集器以从无线传感器网络(WSN)收集传感数据。由于传感器的存储容量和使用寿命随着科学技术的发展而增加,因此传感器可以存储有关监视区域的越来越多的传感数据。但是,由于无人机的能量限制,我们无法在有限的时间内从WSN收集所有数据。因此,在本文中,我们研究了最大化数据收集比例(MDCP)问题:在无人飞行器预算有限的情况下,目标是找到无人飞行器的轨迹,以使所有传感器已最大化。我们首先证明MDCP问题是NP问题。然后提出了两种近似算法设计无人机航迹,并对算法进行了理论分析。最后,我们在不同情况下给出数值结果,以评估所提出算法的有效性。

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