Modeling and Forecasting of Timescale Network Traffic Dynamics in M2M Communications

With an unparalleled number of Machine-to-Machine (M2M) devices being deployed to support a variety of smart-world systems powered by Internet of Things (IoT) technologies, the heterogeneity, uncertainty, and complexity of M2M communications have increased enormously. Thus, how to conduct network resource planning (NRP) has become a challenging issue. In this paper, we propose a novel time series framework (TSF) to model and forecast timescale network traffic dynamics in M2M communications that is capable of providing useful guidance for effective NRP. Specifically, our TSF utilizes the statistical techniques INGARCH(p,q) (integer valued generalized autoregressive conditional heteroskedasticity) and βARMA(p,q) (beta autoregressive moving average) to accurately capture both the internal and external impact factors of the asynchronous and synchronous M2M traffic dynamics over a large time scale, and produces forecasts for multiple upcoming time points by leveraging conditional maximum-likelihood estimators (CMLE). Through a combination of theoretical analysis and extensive simulation, we have validated the modeling and forecasting efficacy of TSF. Our experimental results demonstrate that TSF achieves superior performance with respect to goodness-of-fit and prediction accuracy.