Real Smart Home Data-Assisted Statistical Traffic Modeling for the Internet of Things

The majority of practical studies and analyses in the context of the Internet of Things (IoT) have been carried out assuming that data packet generation follows theoretical models (typically a Poisson process with exponentially distributed packet interarrival times) without previous experimental validation and supporting evidence. In contrast to this approach, this article proposes a novel experimental and mathematical framework to determine statistical models for IoT data traffic. Based on empirical data generated by common smart home devices (e.g., ambient temperature, luminous intensity, atmospheric pressure, and motion sensors) recorded over a full year using an experimental IoT subsystem, this article first shows that real IoT traffic does not follow the Poisson process model conventionally assumed in the literature, but rather depends on the type of application. Consequently, we estimate the empirical statistical distribution of the interarrival between data packets for several smart home applications. The empirical distribution of the packet interarrival times is fitted with some well-established classical statistical distributions using the method of moments as well as maximum-likelihood estimation techniques, and the goodness of fit is quantified using the Kolmogorov-Smirnov (KS) test. Moreover, we also carry out a regression analysis to provide mathematical relations between the distribution parameters and the considered physical input parameters (ambient temperature, luminous intensity, and atmospheric pressure), which is particularly useful in practical scenarios. Furthermore, an exhaustive analysis of the variation of parameters over different time scales and the autocorrelation characteristics of the data packet generation are included as well. In summary, this article provides accurate traffic models suitable for real-life IoT scenarios that can be used for an adequate design and optimization of future communication networks to efficiently support IoT services.