Aggregated Traffic Models for Real-World Data in the Internet of Things

Traffic models play a key role in the analysis, design and simulation of communication networks. The availability of accurate models is essential to investigate the impact of traffic patterns created by the introduction of new services such as those forecasted for the Internet of Things (IoT). The Poisson model has historically been a popular aggregated traffic model and has been extensively used by the IoT research community. However, the Poisson model implicitly assumes an infinite number of traffic sources, which may not be a valid assumption in various plausible application scenarios. The practical conditions under which the Poisson model is valid in the context of IoT have not been fully investigated, in particular under a finite (and possibly reduced) number of traffic sources with random inter-arrival times. In this context, this letter derives exact mathematical models for the packet inter-arrival times of aggregated IoT data traffic based on the superposition of a finite number of traffic sources, each of which is modelled based on real-world experimental data from typical IoT sensors (temperature, light and motion). The obtained exact models are used to explore the validity of the Poisson model, showing that it can be extremely inaccurate when a reduced number of traffic sources is considered. Finally, an illustrative example is presented to show the importance of having accurate and realistic models such as those presented in this letter

Accurate Modelling of IoT Data Traffic Based on Weighted Sum of Distributions

This work proposes a novel mathematical approach to accurately model data traffic for the Internet of Things (IoT). Most of the conventional results on statistical data traffic models for IoT are based on the underlying assumption that the data generation follows standard Poisson or Exponential distribution which lacks experimental validation. However, in some of the use case applications a single statistical distribution is not adequate to provide the best fit for the inter-arrival time of the data packets generation. Based on the real data collected for over 10 weeks using our customized experimental IoT prototype for smart home application, in this paper we have established this very fact, citing barometric air pressure as an example. The statistical distribution of the inter-arrival time between the data packets for a specified barometric pressure fluctuation threshold is initially determined by approximating the best-fit with a set of standard classical distributions. The goodness-of-fit with the empirical data is numerically quantified using Kolmogorov-Smirnov (KS) Test. Furthermore, it is observed that any single standard distribution is unable to provide a good fit which is at least less than 10%. Therefore, a novel weighted distribution scheme is proposed that could provide an acceptable fit. The weighing factor including the location, scaling and weighing parameters of the best fitting distribution are estimated and analyzed. The distribution parameters are finally expressed as a function of the differential pressure value that can be used for different theoretical analysis and network optimization. © 2019 IEEE

Packet Size Optimization for Topology Aware Cognitive Radio Sensor Networks

In this paper, we propose a framework to optimize the packet length and modulation level to determine the optimal packet size (OPS) for topology aware cognitive radio sensor networks (CRSNs) using a variable rate modulation scheme. A generalized network topology with specific node density of the Primary Users (PUs) is accounted to estimate the OPS. Based on stochastic geometry and non-linear optimization techniques, a joint multivariate optimization problem is formulated to determine the OPS for the topology dependent CRSNs