A Spatio-Temporal Model for Information Freshness in Massive Random Access
2026-08-03 • Information Theory
Information TheoryNetworking and Internet Architecture
AI summaryⓘ
The authors study how to keep information from many IoT sensors fresh and accurate when they send updates randomly over a shared wireless channel. They introduce a model that considers both how recent the information is over time and how sensors close to each other can provide overlapping but helpful data in space. By focusing on the receiver’s uncertainty about the true state measured by sensors, the authors analyze two types of receivers – one that forgets old data and one that uses all past data. They then optimize how often sensors should send updates to reduce this uncertainty effectively.
massive connectivityInternet of Things (IoT)age of information (AoI)spatio-temporal modelingrandom access channelslotted ALOHAconditional entropyinformation freshnesstransmission probabilitywireless sensor networks
Authors
Andrea Munari, Alessandro Buratto, Federico Chiariotti, Leonardo Badia, Petar Popovski
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.