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Modeling of Aggregated IoT Traffic and Its Application to an IoT Cloud

机译：物联网总流量建模及其在物联网云中的应用

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摘要

As the Internet of Things (IoT) continues to gain traction in telecommunication networks, a very large number of devices are expected to be connected and used in the near future. In order to appropriately plan and dimension the network, as well as the back-end cloud systems and the resulting signaling load, traffic models are employed. These models are designed to accurately capture and predict the properties of IoT traffic in a concise manner. To achieve this, Poisson process approximations, based on the Palm-Khintchine theorem, have often been used in the past. Due to the scale (and the difference in scales in various IoT networks) of the modeled systems, the fidelity of this approximation is crucial, as, in practice, it is very challenging to accurately measure or simulate large-scale IoT deployments. The main goal of this paper is to understand the level of accuracy of the Poisson approximation model. To this end, we first survey both common IoT network properties and network scales as well as traffic types. Second, we explain and discuss the Palm-Khintiche theorem, how it is applied to the problem, and which inaccuracies can occur when using it. Based on this, we derive guidelines as to when a Poisson process can be assumed for aggregated periodic IoT traffic. Finally, we evaluate our approach in the context of an IoT cloud scaler use case.
机译：随着物联网（IoT）在电信网络中继续受到青睐，预计不久的将来将连接并使用大量设备。为了适当地规划和确定网络以及后端云系统和由此产生的信令负载的规模，请使用流量模型。这些模型旨在精确地捕获和预测IoT流量的属性。为了实现这一点，过去经常使用基于Palm-Khintchine定理的Poisson过程近似。由于建模系统的规模（以及各种IoT网络中规模的差异），这种逼真的逼真度至关重要，因为在实践中，准确地测量或模拟大规模IoT部署非常具有挑战性。本文的主要目的是了解Poisson逼近模型的准确性。为此，我们首先调查常见的物联网网络属性和网络规模以及流量类型。其次，我们解释和讨论Palm-Khintiche定理，如何将其应用到问题上以及使用它时会出现哪些不准确之处。基于此，我们导出了有关何时可以为聚合的定期物联网流量假设泊松过程的指南。最后，我们在IoT云扩展器用例的背景下评估我们的方法。

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