Spatiotemporal Model for Uplink IoT Traffic: Scheduling & Random Access Paradox

TL;DR

This paper develops a spatiotemporal model combining stochastic geometry and queueing theory to compare scheduled and random access uplink strategies in IoT, revealing their scalability and delay trade-offs.

Contribution

It introduces a novel mathematical framework for analyzing IoT uplink schemes, providing insights into their scalability and performance trade-offs.

Findings

RA-UL offers low access delays but limited scalability.

SC-UL supports higher device densities and traffic rates.

The optimal scheme depends on the operational scenario.

Abstract

The Internet-of-things (IoT) is the paradigm where anything will be connected. There are two main approaches to handle the surge in the uplink (UL) traffic the IoT is expected to generate, namely, Scheduled UL (SC-UL) and random access uplink (RA-UL) transmissions. SC-UL is perceived as a viable tool to control Quality-of-Service (QoS) levels while entailing some overhead in the scheduling request prior to any UL transmission. On the other hand, RA-UL is a simple single-phase transmission strategy. While this obviously eliminates scheduling overheads, very little is known about how scalable RA-UL is. At this critical junction, there is a dire need to analyze the scalability of these two paradigms. To that end, this paper develops a spatiotemporal mathematical framework to analyze and assess the performance of SC-UL and RA-UL. The developed paradigm jointly utilizes stochastic geometry and queueing theory. Based on such a framework, we show that the answer to the "scheduling vs. random access paradox" actually depends on the operational scenario. Particularly, RA-UL scheme offers low access delays but suffers from limited scalability, i.e., cannot support a large number of IoT devices. On the other hand, SC-UL transmission is better suited for higher device intensities and traffic rates.