A Traffic Model for Machine-Type Communications Using Spatial Point Processes

TL;DR

This paper introduces a spatial point process-based traffic model for machine-type communications, capturing device and event dynamics, and analyzes temporal traffic characteristics including the impact of event density and device alarm durations.

Contribution

It presents a novel spatial-temporal traffic model using Poisson point processes and Markov chains for machine-to-machine communications, incorporating device and event interactions.

Findings

Traffic rate depends on device and event spatial distribution.

Alarm mode duration significantly affects traffic autocovariance.

Model captures both static and dynamic device behaviors.

Abstract

A source traffic model for machine-to-machine communications is presented in this paper. We consider a model in which devices operate in a regular mode until they are triggered into an alarm mode by an alarm event. The positions of devices and events are modeled by means of Poisson point processes, where the generated traffic by a given device depends on its position and event positions. We first consider the case where devices and events are static and devices generate traffic according to a Bernoulli process, where we derive the total rate from the devices at the base station. We then extend the model by defining a two-state Markov chain for each device, which allows for devices to stay in alarm mode for a geometrically distributed holding time. The temporal characteristics of this model are analyzed via the autocovariance function, where the effect of event density and mean holding time are shown.