IoT网络中的航空数据融合与场估计：无人机悬停与行进时间困境
Aeronautical Data Aggregation and Field Estimation in IoT Networks: Hovering & Traveling Time Dilemma of UAVs
摘要：
下一个信息革命时代将依赖于从广泛分布在我们环境中的大量设备中聚合大数据。这些设备中的大多数预计将是低复杂性，低成本和有限的电源，这对网络操作施加了严格的约束。在这方面，本文研究了通过无人机（UAV）从有限空间域的航空数据聚合和场估计。无人机不是通过无线节点将数据融合、中继和路由到固定位置的接入点，而是飞越现场并收集两个重要任务所需的数据：数据汇总和现场评估。为了完成这些任务，感兴趣的领域被划分为几个子区域，无人机悬停在这些子区域上以从底层节点收集样本。为此，我们制定和解决一个优化问题，以尽量减少总的悬停和旅行时间的每个任务。虽然前者需要从现场收集规定的平均数量的样本，但后者确保对于给定的现场空间相关模型，现场值的平均均方估计误差在任何点都不超过预定阈值。通过优化子区域的数量、每个子区域的面积、悬停位置、每个位置处的悬停时间以及悬停位置之间的轨迹来实现这些目标。所提出的配方被证明是np-hard混合整数问题，因此，提出了一个解耦的启发式解决方案。结果表明，存在一个最佳数量的子区域，平衡悬停和旅行时间之间的权衡，使收集所需的样本的总时间最小化。
英文摘要：
The next era of information revolution will rely on aggregating big data from massive numbers of devices that are widely scattered in our environment. Most of these devices are expected to be of low-complexity, low-cost, and limited power supply, which impose stringent constraints on the network operation. In this regard, this paper investigates aerial data aggregation and field estimation from a finite spatial field via an unmanned aerial vehicle (UAV). Instead of fusing, relaying, and routing the data across the wireless nodes to fixed locations access points, a UAV flies over the field and collects the required data for two prominent missions; data aggregation and field estimation. To accomplish these tasks, the field of interest is divided into several subregions over which the UAV hovers to collect samples from the underlying nodes. To this end, we formulate and solve an optimization problem to minimize total hovering and traveling time of each mission. While the former requires the collection of a prescribed average number of samples from the field, the latter ensures for a given field spatial correlation model that the average mean-squared estimation error of the field value is no more than a predetermined threshold at any point. These goals are fulfilled by optimizing the number of subregions, the area of each subregion, the hovering locations, the hovering time at each location, and the trajectory traversed between hovering locations. The proposed formulation is shown to be np-hard mixed integer problem, and hence, a decoupled heuristic solution is proposed. The results show that there exists an optimal number of subregions that balance the tradeoff between hovering and traveling times such that the total time for collecting the required samples is minimized.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
