A Spatio-Temporal Model for Information Freshness in Massive Random Access

2026-08-03

AI Summary

This work addresses the challenge of optimizing information freshness for massive IoT devices uploading data over a random access channel. It introduces spatial correlation into age-of-information modeling for the first time, integrating temporal age metrics with spatially coordinated observations to formulate a spatiotemporal information freshness model. Device reporting behavior is characterized through successful transmission probability and the accuracy of received updates, while receiver-side uncertainty is quantified using conditional entropy. Under the slotted ALOHA protocol, closed-form expressions for the performance of both memoryless and full-history-aware receivers are derived. The transmission probability of nodes is then optimized to minimize expected uncertainty, significantly enhancing both the timeliness and accuracy of the received information.

Abstract

Massive connectivity, a key building block of 5G, is expected to play an important role in the next generation of wireless systems, and its expected requirements are being revolutionized through the modeling of the information dynamics related to the vast numbers of Internet of things (IoT) devices. Motivated by this, the present paper introduces a model that captures the spatio-temporal nature of freshness of information sent via random access channel policies from an extremely large set of IoT devices via simple scalar parameters, i.e., the probability of success and accuracy of received updates. There are many information freshness metrics, starting from the age of information (AoI), all of which are proxies for the actual application performance, characterized over the temporal dimension. Our model adds the spatial dimension to this picture, observing that sensors distributed over the same area may have a strong correlation, and information from multiple close-by sensors may improve the overall accuracy of the receiver. We focus on characterizing the uncertainty of the receiver, expressed through the conditional entropy, considering a network of partially reliable, spatially distributed sensors observing the same process and reporting their measurements over a slotted ALOHA channel. We consider a simple forgetful receiver and a more complete model which accounts for the full history of past observations, deriving their performance, and optimizing the transmission probability of nodes to minimize the expected uncertainty.

Problem

• Information Freshness
• Massive Random Access
• Spatio-Temporal Modeling
• Age of Information
• Conditional Entropy

Innovation

• Spatio-Temporal Modeling
• Information Freshness
• Massive Random Access
• Conditional Entropy
• IoT Connectivity