Measurements, performance and analysis of LoRa FABIAN, a real-world implementation of LPWAN

TLDR

IoT connectivity has traditionally relied on multi‑hop mesh networks or cellular technologies, but Low‑Power Wide Area networks (LPWAN) challenge these by offering low‑rate, long‑range transmission in unlicensed sub‑GHz bands. This study evaluates the performance of a star‑topology LoRa network, LoRa FABIAN, deployed in Rennes. We conducted extensive performance measurements by generating realistic traffic between IoT nodes and LoRa stations in the LoRa FABIAN stack, extracting metrics such as packet error rate, RSSI, and SNR, and made the data and tools publicly available. The results reveal insights into LoRa network performance and inform evaluation methods for such networks.

Abstract

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.