Given the explosive growth of Internet of Things (IoT) devices ranging from the 2D ground to the 3D space, it is a necessity to establish a 3D spectrum map to comprehensively present and effectively manage the 3D spatial spectrum resources in smart city infrastructures. By leveraging the popularity and location flexibility of unmanned aerial vehicles (UAVs), we are able to execute spatial sampling with these emerging flying spectrum-monitoring devices (SMDs) at will. In this article, we first present a brief survey to show the state-of-the-art studies on spectrum mapping. Then we introduce the 3D spectrum mapping model. Next, we propose a 3D spectrum mapping framework that is composed of pre-sampling, spectrum situation estimation, UAV deployment, and spectrum recovery. Therein we develop a region-of-interest-driven UAV deployment scheme, which selects new sampling points of the highest estimated interest and the lowest energy cost iteratively. Meanwhile, we slice the entire 3D spectrum map into a series of "images" and "repair" those unsampled locations. Furthermore, we provide an exemplary case study on 3D spectrum mapping, where, for example, an important event is being held, and the entire spectrum situation needs to be monitored in real time to deal with malicious interference sources. Lastly, the challenges and open issues are discussed.

中文翻译：

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基于ROI驱动的无人机部署的3D频谱映射

考虑到从2D地面到3D空间的物联网（IoT）设备的爆炸性增长，有必要建立3D频谱图以全面呈现和有效管理智慧城市基础设施中的3D空间频谱资源。通过利用无人飞行器（UAV）的普及性和位置灵活性，我们能够随意使用这些新兴的飞行频谱监测设备（SMD）进行空间采样。在本文中，我们首先进行一个简短的调查，以显示有关频谱映射的最新研究。然后我们介绍了3D频谱映射模型。接下来，我们提出一个3D频谱映射框架，该框架由预采样，频谱状况估计，UAV部署和频谱恢复组成。在此，我们开发了一个由兴趣区域驱动的无人机部署方案，从而以迭代方式选择具有最高估计兴趣和最低能源成本的新采样点。同时，我们将整个3D频谱图切成一系列“图像”，并“修复”那些未采样的位置。此外，我们提供了一个有关3D频谱映射的示例性案例研究，例如，正在举行一个重要事件，并且需要实时监视整个频谱状况以应对恶意干扰源。最后，讨论了挑战和未解决的问题。一个重要的事件正在举行，整个频谱情况需要实时监控以处理恶意干扰源。最后，讨论了挑战和未解决的问题。一个重要的事件正在举行，整个频谱情况需要实时监控以处理恶意干扰源。最后，讨论了挑战和未解决的问题。