Delay Optimal UAV Trajectory Planning for Secure Data Collection from Mobile IoT Networks
Delay Optimal UAV Trajectory Planning for Secure Data Collection from Mobile IoT Networks
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DOI：
10.1109/icit58465.2023.10143110
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发表时间：
2023-04
期刊：
影响因子：
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通讯作者：
Amirahmad Chapnevis;E. Bulut
中科院分区：
文献类型：
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作者：
Amirahmad Chapnevis;E. Bulut
无人驾驶飞行器（uav）最近被用于从监视到通信的许多应用中。由于部署成本低、灵活性强，无人机还可以协助从地面物联网（IoT）设备收集数据。由于无人机的能量和飞行时间有限，无人机在数据采集过程中的轨迹规划至关重要。虽然有几项研究以不同的目标来研究这个问题，但仍然需要找到从移动物联网设备收集数据的最佳无人机路径，同时考虑延迟和安全收集数据是主要关注的问题。在这篇正在进行的论文中，我们研究了这个问题，其中无人机的目标是在其飞行期间最大限度地减少从地面物联网设备收集数据的平均或最大延迟，同时远离其路径上的潜在窃听者。我们使用整数线性规划（ILP）对问题建模，并给出不同场景的结果。我们的下一个目标是开发一种基于强化学习的解决方案，该解决方案可以提供接近最优基于ILP的结果，但也适用于现实场景。
Unmanned aerial vehicles (UAVs) have recently been used in many applications from surveillance to communication. UAVs can also assist the process of data collection from ground Internet of Things (IoT) devices thanks to the low deployment cost and flexibility. Since the energy and flight time of UAVs is limited, the trajectory planning for the UAVs during this data collection process is vital. While there are several studies that look at this problem with varying objectives, there is still a need for finding the optimal UAV path for data collection from mobile IoT devices with both the delay and secure collection of data in mind as the main concern. In this work-in-progress paper, we study this problem where a UAV aims to minimize the average or maximum delay of the collected data from ground IoT devices within its flight duration while also staying away from potential eavesdroppers on its path. We model the problem using Integer Linear Programming (ILP) and present results for different scenarios. Our next goal is to develop a reinforcement learning based solution that can provide results that are close to optimal ILP based results but also applicable to real-life scenarios.