With the increasing adoption of fifth-generation (5G) networks and wide bandwidth availability, unmanned aerial vehicles (UAVs) should now be practicable to stream and operate at different altitudes and upload incredibly large quantities of data from sensors. We develop a novel intelligent sensing framework (Choi, 2020) so that sensing and communication occur on demand, according to a given query. Our query-based Data Aided Sensing (DAS) approach differs from existing methods as we start with selecting relevant nodes based on the query and then allocating UAVs for collecting measurements. We develop an economical and efficient approach to collect measurements from massive passive sensors, which adaptively minimize the entropy gap while scheduling UAVs to sensors. At the heart of our solution is a mathematical approach for estimating entropy, cost and energy consumption, thereby deploying dynamically optimal UAV-sensor association (e.g., optimizing entropy and employing bipartite graph). Our system has been evaluated with extensive simulations, providing insightful observations and findings.

中文翻译：

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使用无人机对数据辅助传感进行建模以实现高效的数据收集

随着第五代 (5G) 网络的日益普及和广泛的带宽可用性，无人机 (UAV) 现在应该可以在不同高度进行流式传输和操作，并从传感器上传大量数据。我们开发了一种新颖的智能传感框架 (Choi, 2020)，以便根据给定的查询按需进行传感和通信。我们基于查询的数据辅助传感 (DAS) 方法与现有方法不同，因为我们首先根据查询选择相关节点，然后分配无人机以收集测量值。我们开发了一种经济高效的方法来从大量无源传感器收集测量值，该方法在将无人机调度到传感器时自适应地最小化熵间隙。我们解决方案的核心是一种估计熵的数学方法，成本和能源消耗，从而动态部署最佳无人机传感器关联（例如，优化熵和采用二部图）。我们的系统已经过广泛的模拟评估，提供了有见地的观察和发现。