Network simulation is a tool used to analyse and predict the performance of Industrial Internet of Things deployments while dealing with the complexity of real testbeds. Large network deployments with complex protocols such as Transmission Control Protocol are subject to chaos-theory behaviour, i.e. small changes in the implementation of the protocol stack or simulator behaviour may result in large differences in the performance results. We present the results of simulating two different scenarios using three simulators. The first scenario focuses on the Incast phenomenon in a local area network where sensor data are collected. The second scenario focuses on a congested link traversed by the collected measurements. The performance metrics obtained from the simulators are compared among them and with ground-truth obtained from real network experiments. The results demonstrate how subtle implementation differences in network simulators impact performance results, and how network engineers must consider these differences.Index Terms-Computer simulation, Internet of Things, Reproducibility I. INTRODUCTIONT HE Industrial Internet of Things (IIoT) is adding large numbers of devices to communication networks. In this era, new devices are connected and old ones are removed, equipment from different vendors is added, old applications stop being used and new ones are developed, controller servers are moved from one switch to another or even to a remote site, new links with increased capacity are added, and new routing protocols are deployed, perhaps without removing old ones, all of this at a really fast pace. Any of these changes can yield consequences in network performance that are very difficult to predict. In complex systems such as modern networks, because of non-linearities and synchronisations, small changes can yield large-scale consequences. This phenomenon has been referred to as the amplification principle [1].At present, it is unfeasible to predict network behaviour and performance using analytical tools. This is because a large number of elements must be considered in the model, including non-deterministic behaviours (e.g., user actions).
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