Wi-Fi networks are extensively used to provide Internet access to end-users and to deploy applications at the edge. By playing a major role in modern networking, Wi-Fi networks are getting bigger and denser. However, studying their performance at large-scale and in a reproducible manner remains a challenging task. Current solutions include real experiments and simulations. While the size of experiments is limited by their financial cost and potential disturbance of commercial networks, the simulations also lack scalability due to their models' granularity and computational runtime. In this paper, we introduce a new Wi-Fi model for large-scale simulations. This model, based on flow-level simulation, requires fewer computations than state-of-the-art models to estimate bandwidth sharing over a wireless medium, leading to better scalability. Comparing our model to the already existing Wi-Fi implementation of ns-3, we show that our approach yields to close performance evaluations while improving the runtime of simulations by several orders of magnitude. Using this kind of model could allow researchers to obtain reproducible results for networks composed of thousands of nodes much faster than previously. CCS CONCEPTS• Networks → Network simulations; Wireless local area networks.

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Wi-Fi devices are ubiquitous, thus they have been extensively studied to understand, for example, the impact of different channel conditions and network properties over network performance. However, improving network performance without considering energy consumption can lead to critical issues: battery depletion, higher costs, and increased latency. Existing works provide algorithms and techniques for more efficient use of energy for Wi-Fi communication, especially in the case of IoT networks, limited by battery capacity. But the evergrowing number of Wi-Fi devices along with the increase in traffic and heterogeneity of current networks make measuring the energy footprint of Wi-Fi communication particularly complex, especially at a large scale. Existing simulation models to study the energy consumption of Wi-Fi devices either suffer from scalability issues due to their fine granularity, or lack realism hindering their usage in practice. In this paper, we propose a power model tackling these scalability and accuracy issues through the use of a flow-based simulation model. By comparing the accuracy and performance of our model to state-of-the-art solution, we show that our approach achieves accurate energy predictions on largescale and heterogeneous network infrastructures. Our flow-level model allows us to simulate the energy consumption of 800 nodes in a few seconds compared to more fine-grained simulators such as ns-3 that require more than 8 hours under the same scenario, with similar accuracy.