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AoI-Minimal Trajectory Planning and Data Collection in UAV-Assisted Wireless Powered IoT Networks
IEEE Internet of Things Journal ( IF 10.6 ) Pub Date : 2020-07-29 , DOI: 10.1109/jiot.2020.3012835
Huimin Hu , Ke Xiong , Gang Qu , Qiang Ni , Pingyi Fan , Khaled Ben Letaief

This article investigates the unmanned aerial vehicle (UAV)-assisted wireless powered Internet-of-Things system, where a UAV takes off from a data center, flies to each of the ground sensor nodes (SNs) in order to transfer energy and collect data from the SNs, and then returns to the data center. For such a system, an optimization problem is formulated to minimize the average Age of Information (AoI) of the data collected from all ground SNs. Since the average AoI depends on the UAV’s trajectory, the time required for energy harvesting (EH) and data collection for each SN, these factors need to be optimized jointly. Moreover, instead of the traditional linear EH model, we employ a nonlinear model because the behavior of the EH circuits is nonlinear by nature. To solve this nonconvex problem, we propose to decompose it into two subproblems, i.e., a joint energy transfer and data collection time allocation problem and a UAV’s trajectory planning problem. For the first subproblem, we prove that it is convex and give an optimal solution by using Karush–Kuhn–Tucker (KKT) conditions. This solution is used as the input for the second subproblem, and we solve optimally it by designing dynamic programming (DP) and ant colony (AC) heuristic algorithms. The simulation results show that the DP-based algorithm obtains the minimal average AoI of the system, and the AC-based heuristic finds solutions with near-optimal average AoI. The results also reveal that the average AoI increases as the flying altitude of the UAV increases and linearly with the size of the collected data at each ground SN.

中文翻译:

无人机辅助无线物联网网络中的AoI-最小轨迹规划和数据收集

本文研究了无人机(UAV)辅助的无线物联网系统,其中无人机从数据中心起飞,飞向每个地面传感器节点(SN),以传输能量并收集数据从SN,然后返回到数据中心。对于这样的系统,提出了一个优化问题,以最小化从所有地面SN收集的数据的平均信息年龄(AoI)。由于平均AoI取决于无人机的轨迹,每个SN的能量收集(EH)和数据收集所需的时间,因此需要共同优化这些因素。而且,代替传统的线性EH模型,我们使用非线性模型,因为EH电路的行为本质上是非线性的。为了解决此非凸问题,我们建议将其分解为两个子问题,即 联合的能量传输和数据收集时间分配问题以及无人机的航迹计划问题。对于第一个子问题,我们证明了它是凸的,并通过使用Karush–Kuhn–Tucker(KKT)条件给出了最优解。该解决方案用作第二个子问题的输入,我们通过设计动态编程(DP)和蚁群(AC)启发式算法来对其进行最佳解决。仿真结果表明,基于DP的算法获得了系统的最小平均AoI,基于AC的启发式算法找到了具有接近最佳平均AoI的解。结果还表明,平均AoI随无人机飞行高度的增加而增加,并且与每个地面SN处所收集数据的大小呈线性关系。对于第一个子问题,我们证明了它是凸的,并通过使用Karush–Kuhn–Tucker(KKT)条件给出了最优解。该解决方案用作第二个子问题的输入,我们通过设计动态编程(DP)和蚁群(AC)启发式算法来对其进行最佳解决。仿真结果表明,基于DP的算法获得了系统的最小平均AoI,基于AC的启发式算法找到了具有接近最佳平均AoI的解。结果还表明,平均AoI随无人机飞行高度的增加而增加,并且与每个地面SN处所收集数据的大小呈线性关系。对于第一个子问题,我们证明了它是凸的,并通过使用Karush–Kuhn–Tucker(KKT)条件给出了最优解。该解决方案用作第二个子问题的输入,我们通过设计动态编程(DP)和蚁群(AC)启发式算法来对其进行最佳解决。仿真结果表明,基于DP的算法获得了系统的最小平均AoI,基于AC的启发式算法找到了具有接近最佳平均AoI的解。结果还表明,平均AoI随无人机飞行高度的增加而增加,并且与每个地面SN处所收集数据的大小呈线性关系。并且我们通过设计动态规划(DP)和蚁群(AC)启发式算法来对其进行优化。仿真结果表明,基于DP的算法获得了系统的最小平均AoI,基于AC的启发式算法找到了具有接近最佳平均AoI的解。结果还表明,平均AoI随无人机飞行高度的增加而增加,并且与每个地面SN处所收集数据的大小呈线性关系。并且我们通过设计动态规划(DP)和蚁群(AC)启发式算法来对其进行优化。仿真结果表明,基于DP的算法获得了系统的最小平均AoI,基于AC的启发式算法找到了具有接近最佳平均AoI的解。结果还表明,平均AoI随无人机飞行高度的增加而增加,并且与每个地面SN处所收集数据的大小呈线性关系。
更新日期:2020-07-29
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