Scalability Analysis of Large-Scale LoRaWAN Networks in ns-3

By Floris Van den Abeele, Jetmir Haxhibeqiri, Ingrid Moerman, Jeroen Hoebeke. IEEE Internet of Things Journal, 2017

323 citations recorded by the source corpus.

As LoRaWAN networks are actively being deployed in the field, it is important to comprehend the limitations of this low power wide area network technology. Previous work has raised questions in terms of the scalability and capacity of LoRaWAN networks as the number of end devices grows to hundreds or thousands per gateway. Some works have modeled LoRaWAN networks as pure ALOHA networks, which fails to capture important characteristics such as the capture effect and the effects of interference. Other works provide a more comprehensive model by relying on empirical and stochastic techniques. This paper uses a different approach where a LoRa error model is constructed from extensive complex baseband bit error rate simulations and used as an interference model. The error model is combined with the LoRaWAN MAC protocol in an ns-3 module that enables to study multichannel, multispreading factor, multigateway, bi-directional LoRaWAN networks with thousands of end devices. Using the LoRaWAN ns-3 module, a scalability analysis of LoRaWAN shows the detrimental impact downstream traffic has on the delivery ratio of confirmed upstream traffic. The analysis shows that increasing gateway density can ameliorate but not eliminate this effect, as stringent duty cycle requirements for gateways continue to limit downstream opportunities.

Subfields: Computer Networks and Communications, Electrical and Electronic Engineering

View Paper: https://www.semanticscholar.org/paper/ec633f8bba8157301d5e8d826b891286328b6a45

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Citations in the corpus

A Survey on Scalable LoRaWAN for Massive IoT: Recent Advances, Potentials, and Challenges (2022, background): “The NS-3 simulation in [160] revealed that increasing the GW density can not eliminate all the scalability issues since the duty-cycle restriction remains applied for GW which restricts its downstream traffic.”
Adaptive configuration of lora networks for dense IoT deployments (2018, methodology): “[8] presented a LoRa simulator based on ns-3.”
A Survey of LoRaWAN for IoT: From Technology to Application (2018, methodology): “The physical layer model in [40] is based on a LoRa PHY error model that is determined using an extensive set of physical layer simulations in MATLAB.”
A Survey on LoRa Networking: Research Problems, Current Solutions, and Open Issues (2019, methodology): “• Network density can be scaled-up by adding more gateways and personalizing transmission parameters like data rate and channel allocation using ADR for each enddevice [25], [66], [67], [69].”