Data collection from LoRaWAN sensor network by UAV gateway: design, empirical results and dataset

Gianmarco Canello, Silvia Mignardi, K. Mikhaylov, C. Buratti, T. Hänninen
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Abstract

Collecting data from Internet-of-Things (IoT) devices, especially the variety of sensors dispersed in the environment, is an increasingly important and difficult task. Several long-range radio-access technologies, such as low-power wide-area networks (LPWAN) and specifically LoRaWAN, have been proposed to address this challenge. However, until now, the key focus of the related studies has been on static terrestrial LPWAN deployments. In this study, we depart from this vision and investigate the practical feasibility and performance of a LoRaWAN gateway (GW) on a flying platform, specifically - an unmanned aerial vehicle (UAV). The key contributions of this study are (i) the design and field-testing of a packet-sniffer-based mobile LoRaWAN GW prototype, allowing collection of the data from LoRaWAN networks, including the already deployed ones; (ii) the open-publication of the data collected during our experimental campaign in the 426 LoRaWAN sensor node network of the University of Oulu illustrating the performance of different drone trajectories; (iii) the initial results of the system's performance analysis, revealing some interesting trends and setting goals for further studies, and pinpointing the lessons learned during the experimental campaign. Our empirical findings suggest that the Travelling Salesman Problem (TSP) trajectory is the most effective moving trajectory for the number of packets collected and the average energy consumed per packet collected.
无人机网关LoRaWAN传感器网络数据采集:设计、实证结果与数据集
从物联网(IoT)设备中收集数据,特别是分散在环境中的各种传感器,是一项越来越重要和困难的任务。一些远程无线接入技术,如低功耗广域网(LPWAN),特别是LoRaWAN,已经被提出来应对这一挑战。然而,到目前为止,相关研究的重点是静态地面LPWAN部署。在本研究中,我们从这一愿景出发,研究了LoRaWAN网关(GW)在飞行平台上的实际可行性和性能,特别是无人机(UAV)。本研究的主要贡献是:(i)基于数据包嗅探器的移动LoRaWAN GW原型的设计和现场测试,允许从LoRaWAN网络收集数据,包括已经部署的网络;(ii)公开发布我们在奥卢大学426 LoRaWAN传感器节点网络的实验活动中收集的数据,说明不同无人机轨迹的性能;(iii)系统性能分析的初步结果,揭示一些有趣的趋势和设定进一步研究的目标,并指出在实验活动中吸取的教训。我们的实证研究结果表明,旅行推销员问题(TSP)轨迹是收集数据包数量和收集数据包平均能量消耗的最有效的移动轨迹。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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