Nowadays, Unmanned Aerial Vehicles (UAVs) represent a significant aid on scenarios where fixed, ground infrastructures are temporarily or permanently not available; this is the case of large-scale applications of the Internet of Things (IoTs), e.g. smart city and agriculture 3.0, where the UAVs can be employed as mobile data mules and gather the data from Wireless Ground Sensors (WGSs). UAV-aided wireless sensor networks (WSNs) introduce considerable advantages both in terms of performance and costs since they avoid the need of error-prone multi-hop communications, and also the installation of static gateways; at the same time, they pose formidable research challenges for their implementation, like the synchronization issue between the UAV and the WGS and the path planning, which should take into account the extremely limited flight autonomy of the UAVs. In this paper, we address both the issues above by proposing BEE-DRONES, a novel framework for large-scale, ultra low-power UAV-aided WSNs. In order to mitigate the synchronization problem, we investigate the utilization of passive Wake-up Radio (WR) technology on the WGSs, and of wireless power transfer from the UAVs: by harvesting the energy from the UAV hovering over it, the WGS is activated only for the short time required to transfer the data toward the mobile sink, while it experiences zero-consumption in sleep mode. We investigate the performance of passive WR-based WGS through real measurements, under different WGS-UAV distances and antenna orientations. Then, based on such results, we formulate the joint WGS scheduling and UAV path planning problem, where the goal is to determine the optimal trajectory of the UAVs activating the WR-based WGSs while taking into account the Value of the Sensing (VoS) as well as the total lifetime of the WSN. The original problem is transformed into a multi-commodity flow problem, and both centralized and distributed heuristics over the multi-graph are proposed. Finally, we evaluate the proposed algorithms through extensive OMNeT++ simulations; the results demonstrate the gain of BEE-DRONES in terms of extended lifetime compared to traditional, non WR-based solutions (e.g. duty-cycle), and in terms of reduced data-correlation compared to non VoS-aware path planning solutions.

如今，无人飞行器（UAV）在临时或永久不可用固定地面基础设施的情况下提供了重要的帮助。物联网（IoT）的大规模应用就是这种情况，例如智慧城市和农业3.0，在这些应用中，无人机可以用作移动数据data，并从无线地面传感器（WGS）收集数据。无人机辅助的无线传感器网络（WSN）在性能和成本方面都具有相当大的优势，因为它们避免了容易出错的多跳通信以及静态网关的安装；同时，它们给实施带来了巨大的研究挑战，例如无人机和WGS之间的同步问题以及路径规划，这应考虑到无人机的飞行自主性极为有限。在本文中，我们通过提议解决以上两个问题蜜蜂无人机，这是用于大规模，超低功耗无人机辅助WSN的新颖框架。为了减轻同步问题，我们研究了WGS上无源唤醒无线电（WR）技术的使用以及从无人飞机传输无线电力的方法：通过从悬停在其上的无人飞行器收集能量，激活了WGS仅在将数据传输到移动接收器所需的短时间内，而在睡眠模式下其消耗为零。我们通过在不同的WGS-UAV距离和天线方向下的实际测量来研究基于无源WR的WGS的性能。然后，基于这些结果，我们制定了WGS调度和无人机路径规划的联合问题，此处的目标是确定激活基于WR的WGS的无人机的最佳轨迹，同时考虑传感（VoS）的值以及WSN的总寿命。将原始问题转化为多商品流问题，并提出了针对多图的集中式和分布式启发式算法。最后，我们通过广泛的OMNeT ++仿真评估提出的算法；结果证明了与传统的非基于WR的解决方案（例如，占空比）相比，BEE-DRONES的使用寿命更长，与非VoS的路径规划解决方案相比，BEE-DRONES的数据相关性降低。