01696nas a2200133   4500000000100000008004100001260004700042100002200089700002200111245009600133856006700229520124400296020002201540       2026                            d  c01/2026bSpringer Nature SwitzerlandaCham1 aArtur Frankiewicz1 aKrzysztof Grochla00aUsing Time Series Analysis to Generate Synthetic Traffic Data for LoRa Network Simulation  uhttps://link.springer.com/chapter/10.1007/978-3-032-04197-5_163 a<p>Modelling radio signal attenuation in Low Power Wide Area Networks (LPWANs) is a complex challenge due to the numerous factors influencing signal propagation and attenuation. Recent advancements in artificial intelligence have enabled the use of neural network-based time series analysis to develop radio attenuation models suitable for radio simulators. In this paper, we evaluate the feasibility of such AI-driven models for discrete event simulations. To train and validate these models, we utilize data collected from a large-scale LoRaWAN network that has been operational for over six years. The trained model is then applied to generate synthetic data for radio network simulations. We compare the AI-generated data with conventional distribution-based generators, assessing their effectiveness in modelling real-world events and analysing the statistical distribution of the generated data. Finally, we discuss the benefits and limitations of using AI-based models for simulation purposes. While traditional generators typically produce data distributions that closely mirror real data, AI algorithms are more effective at capturing specific patterns found in actual datasets what is challenging with conventional methods.</p>
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