Spatio-Temporal Information Freshness for Remote Source Monitoring in IoT Systems

TL;DR

This paper introduces a spatio-temporal model for information freshness in IoT remote monitoring, highlighting the importance of considering spatial correlations to improve accuracy and energy efficiency over traditional age-based methods.

Contribution

It presents a novel spatio-temporal framework for information freshness that accounts for spatial correlations, addressing a gap in existing age of information research.

Findings

Pure age-oriented schemes select overly broad communication ranges.

Spatio-temporal approach improves estimate accuracy.

Energy efficiency is enhanced with the proposed model.

Abstract

The widespread adoption of age of information (AoI) as a meaningful and analytically tractable information freshness metric has led to a wide body of work on the timing performance of Internet of things (IoT) systems. However, the spatial correlation inherent to environmental monitoring has been mostly neglected in the recent literature, due to the significant modeling complexity it introduces. In this work, we address this gap by presenting a model of spatio-temporal information freshness, considering the conditional entropy of the system state in a remote monitoring scenario, such as a low-orbit satellite collecting information from a wide geographical area. Our analytical results show that purely age-oriented schemes tend to select an overly broad communication range, leading to inaccurate estimates and energy inefficiency, both of which can be mitigated by adopting a spatio-temporal approach.