Spatiotemporal Stochastic Modeling of IoT Enabled Cellular Networks: Scalability and Stability Analysis
Mohammad Gharbieh, Hesham ElSawy, Ahmed Bader, and Mohamed-Slim, Alouini

TL;DR
This paper develops a stochastic spatiotemporal model to analyze the scalability and stability of cellular networks supporting massive IoT device connectivity, considering interference and traffic requirements.
Contribution
It introduces a novel traffic-aware mathematical model based on stochastic geometry and queueing theory for IoT cellular uplink analysis, evaluating multiple transmission strategies.
Findings
Cellular networks face scalability challenges with massive IoT traffic.
Different transmission strategies impact network stability and performance.
The model provides insights into effective strategies for IoT support in cellular networks.
Abstract
The Internet of Things (IoT) is large-scale by nature, which is manifested by the massive number of connected devices as well as their vast spatial existence. Cellular networks, which provide ubiquitous, reliable, and efficient wireless access, are natural candidates to provide the first-mile access for the data tsunami to be generated by the IoT. However, cellular networks may have scalability problems to provide uplink connectivity to massive numbers of connected things. To characterize the scalability of cellular uplink in the context of IoT networks, this paper develops a traffic-aware spatiotemporal mathematical model for IoT devices supported by cellular uplink connectivity. The developed model is based on stochastic geometry and queueing theory to account for the traffic requirement per IoT device, the different transmission strategies, and the mutual interference between the IoT…
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