A Novel Mathematical Framework for Modeling Application-Specific IoT Traffic
Dana Haj Hussein, Mohamed Ibnkahla
Abstract
Traffic modeling is a valuable tool for simulating traffic characteristics and assessing the effectiveness of new network mechanisms and protocol designs. The emergence of the Internet of Things (IoT) has led to a growing interest in IoT traffic modeling due to the unique characteristics of IoT traffic, such as sudden data bursts and application-dependent traffic characteristics. The focus of the literature has been on modeling the arrival distribution of IoT traffic. However, this approach fails to capture important characteristics of time-series traffic, such as IoT traffic behaviors and seasonality patterns. Such characteristics provide crucial insights for the effective management and optimization of IoT networks. By exploiting time-series characteristics, dynamic resource allocation mechanisms can be designed instead of resource provisioning for peak usage. Additionally, comprehending the traffic generation behavior of IoT sensors can provide insight into the energy consumption of the sensor layer, which has a multitude of uses. In this article, we present a novel IoT traffic modeling framework called the tiered Markov-modulated stochastic process (TMMSP). The TMMSP framework can produce application-specific IoT time-series traffic traces that mimic the behaviors, e.g., the temporal dynamics, of real IoT traffic. Our results illustrate the flexibility and capability of the TMMSP framework in modeling the traffic behaviors of three IoT applications, specifically, telehealth, asset monitoring, and building security applications. Finally, we illustrate how the TMMSP framework can be used to evaluate the performance of an autonomous edge slicing (AES) mechanism.