Scalability analysis of large-scale LoRaWAN networks in ns-3 (1705.05899v1)

Abstract: 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 work 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 multi channel, multi spreading factor, multi gateway, bi-directional LoRaWAN networks with thousands of end devices. Using the lorawan ns-3 module, a scalability analysis of LoRaWAN shows the detrimental impact of downstream traffic 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.

• The paper introduces a comprehensive LoRa error model and an ns-3 simulation framework to analyze large-scale LoRaWAN network scalability.
• A performance analysis revealed that a PER-based spreading factor assignment strategy is superior for different network densities.
• Bidirectional confirmed traffic and gateway duty cycles negatively impact network performance, a problem partially alleviated by increased gateway density.

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

In the paper, "Scalability Analysis of Large-Scale LoRaWAN Networks in ns-3" by Floris Van den Abeele et al., the authors explore the scalability challenges associated with LoRaWAN networks. This paper is motivated by the proliferation of IoT devices that necessitate effective large-scale deployment strategies while maintaining efficient network performance.

Core Contributions

The paper's notable contributions include an error model for the LoRa modulation scheme, a thorough implementation of the LoRaWAN standard within the ns-3 simulator, and an in-depth scalability analysis that evaluates the impact of network parameters on performance. The research extends existing models by incorporating a comprehensive LoRa error model tailored for different coding rates and spreading factors, integrated with the ns-3 environment to simulate multi-channel, multi-spreading factor networks.

Methodology and Findings

Error Modeling and Simulation Framework

The research introduces an error model that simulates LoRa modulations over an AWGN channel, calculated through complex baseband simulations in MATLAB. This model is crucial for accurately determining bit error rates (BER) across different LoRa PHY configurations. The integration of this error model in ns-3 allows for simulating up to 10,000 end devices across varied network setups, examining the effects of gateways, traffic types, data rates, and more.

Scalability Analysis

• Spreading Factor Assignment: Three strategies for assigning spreading factors (SF) are explored — random, fixed, and based on packet error ratio (PER) thresholds. The PER-based allocation strategy demonstrated superior performance across varying network densities, indicating its potential efficacy in dynamic deployment environments.
• Impact of Bidirectional Traffic: The paper highlights the limitations of confirmed upstream transmissions, which despite anticipated improvements due to retransmissions, often result in decreased packet delivery ratios (PDR) due to the burden of acknowledgments. Constrained duty cycles on gateways further exacerbate these challenges.
• Gateway Density: Increasing gateway density positively influences network performance by mitigating some of the negative effects associated with downstream traffic. This acts to partially alleviate gateway saturation and expand the effective communication range of end devices.

Downstream Traffic Effects

The analysis underscores how limited downstream traffic capacity can detrimentally impact network performance, especially in dense network conditions. However, as gateway density increases, the adverse effects are somewhat mitigated.

Implications and Future Work

The findings have critical implications for large-scale IoT deployments using LoRaWAN. They underscore the importance of efficient parameter setting, particularly related to spreading factors and acknowledgment strategies. The authors suggest that increased gateway density can ameliorate some constraints but recognize it cannot fully resolve the saturation issue.

The research opens several avenues for future exploration, such as:

• Refining co-spreading factor interference modeling using stochastic approaches rather than simple noise models.
• Optimizing downstream data rates and exploring structured medium access to further improve network capacity.
• Extending the model to comprehend the interactions between LoRaWAN and other co-located sub-GHz technologies like 802.11ah.

Conclusion

This paper provides valuable insights into the scalability constraints of LoRaWAN networks, offering an enhanced simulation framework within ns-3 that blends empirical and stochastic modeling techniques. The detailed analysis and open-source provision of the simulation tools present researchers and practitioners with practical resources to conduct further research and refine the deployment of LPWAN technologies in IoT ecosystems.