LoRa FABIAN的测量,性能和分析,LPWAN的现实世界实现

Tara Petric, Mathieu Goessens, L. Nuaymi, L. Toutain, A. Pelov
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引用次数: 128

摘要

到目前为止,在不断发展的物联网(IoT)中,连接“事物”的主要方法有两种——一种是基于多跳网状网络,使用短距离技术和未经许可的频谱,另一种是基于远程蜂窝网络技术,使用相应的许可频段。低功耗广域网(LPWAN)中使用的新型连接通过在未经许可的sub-GHz频段使用低速率远程传输技术来挑战这些方法。在本文中,我们在一个这样的星形拓扑网络上进行了性能测试,该网络基于Semtech的LoRa™技术,并部署在雷恩市- LoRa FABIAN。为了检查该网络在一般和特定条件下可以提供的服务质量(QoS),我们进行了一组性能测量。我们通过使用LoRa FABIAN协议栈生成并观察物联网节点和LoRa物联网站之间的流量来执行测试。通过我们的实验设置,我们能够生成与实际应用(如传感器监控)非常相似的流量。这使我们能够在各种条件下提取基本的性能指标,例如数据包错误率(PER),以及与LoRa物理层专门相关的指标,例如接收信号强度指标(RSSI)和信噪比(SNR)。我们的研究结果提供了关于LoRa网络性能的见解,以及对这些类型网络的评估方法。我们收集的测量数据与我们使用的工具一起免费提供。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Measurements, performance and analysis of LoRa FABIAN, a real-world implementation of LPWAN
Up to recently, two main approaches were used for connecting the “things” in the growing Internet of Things (IoT) — one based on multi-hop mesh networks, using short-range technologies and unlicensed spectrum, and the other based on long-range cellular network technologies using corresponding licensed frequency bands. New type of connectivity used in Low-Power Wide Area networks (LPWAN), challenges these approaches by using low-rate long-range transmission technologies in unlicensed sub-GHz frequency bands. In this paper, we do performance testing on one such star-topology network, based on Semtech's LoRa™ technology, and deployed in the city of Rennes — LoRa FABIAN. In order to check the quality of service (QoS) that this network can provide, generally and in given conditions, we conducted a set of performance measurements. We performed our tests by generating and then observing the traffic between IoT nodes and LoRa IoT stations using our LoRa FABIAN protocol stack. With our experimental setup, we were able to generate traffic very similar to the one that can be used by real application such as sensor monitoring. This let us extract basic performance metrics, such as packet error rate (PER), but also metrics related specifically to the LoRa physical layer, such as the Received Signal Strength Indicator (RSSI) and Signal to Noise ratio (SNR), within various conditions. Our findings provide insight about the performance of LoRa networks, but also about evaluation methods for these type of networks. We gathered measurement data that we make freely available together with the tools we used.
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