Information Freshness Analysis of Slotted ALOHA in Gilbert-Elliot Channels

TL;DR

This paper investigates how information freshness metrics behave in large IoT systems using slotted ALOHA over Gilbert-Elliot channels, providing exact formulas for average and peak penalties based on a power law model.

Contribution

It introduces a novel analysis of information freshness using a power law penalty function and derives exact closed-form expressions for key metrics in Gilbert-Elliot channels.

Findings

Exact formulas for average penalty and peak violation probability

Power law penalty function offers a different perspective from traditional age metrics

Analysis applicable to large IoT systems with slotted ALOHA in Gilbert-Elliot channels

Abstract

This letter analyzes a class of information freshness metrics for large IoT systems in which terminals employ slotted ALOHA to access a common channel. Considering a Gilbert- Elliot channel model, information freshness is evaluated through a penalty function that follows a power law of the time elapsed since the last received update, in contrast with the linear growth of age of information. By means of a signal flow graph analysis of Markov processes, we provide exact closed form expressions for the average penalty and for the peak penalty violation probability.