Monitoring Correlated Sources: AoI-based Scheduling is Nearly Optimal

Fuente: arXiv
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Main Authors: Ramakanth, R Vallabh, Tripathi, Vishrant, Modiano, Eytan
Format: Preprint
Published: 2023
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author Ramakanth, R Vallabh
Tripathi, Vishrant
Modiano, Eytan
author_facet Ramakanth, R Vallabh
Tripathi, Vishrant
Modiano, Eytan
contents We study the design of scheduling policies to minimize monitoring error for a collection of correlated sources, where only one source can be observed at any given time. We model correlated sources as a discrete-time Wiener process, where the increments are multivariate normal random variables, with a general covariance matrix that captures the correlation structure between the sources. Under a Kalman filter-based optimal estimation framework, we show that the performance of all scheduling policies oblivious to instantaneous error, can be lower and upper bounded by the weighted sum of Age of Information (AoI) across the sources for appropriately chosen weights. We use this insight to design scheduling policies that are only a constant factor away from optimality, and make the rather surprising observation that AoI-based scheduling that ignores correlation is sufficient to obtain performance guarantees. We also derive scaling results that show that the optimal error scales roughly as the square of the dimensionality of the system, even in the presence of correlation. Finally, we provide simulation results to verify our claims.
format Preprint
id arxiv_https___arxiv_org_abs_2312_16813
institution arXiv
publishDate 2023
record_format arxiv
spellingShingle Monitoring Correlated Sources: AoI-based Scheduling is Nearly Optimal
Ramakanth, R Vallabh
Tripathi, Vishrant
Modiano, Eytan
Networking and Internet Architecture
Systems and Control
We study the design of scheduling policies to minimize monitoring error for a collection of correlated sources, where only one source can be observed at any given time. We model correlated sources as a discrete-time Wiener process, where the increments are multivariate normal random variables, with a general covariance matrix that captures the correlation structure between the sources. Under a Kalman filter-based optimal estimation framework, we show that the performance of all scheduling policies oblivious to instantaneous error, can be lower and upper bounded by the weighted sum of Age of Information (AoI) across the sources for appropriately chosen weights. We use this insight to design scheduling policies that are only a constant factor away from optimality, and make the rather surprising observation that AoI-based scheduling that ignores correlation is sufficient to obtain performance guarantees. We also derive scaling results that show that the optimal error scales roughly as the square of the dimensionality of the system, even in the presence of correlation. Finally, we provide simulation results to verify our claims.
title Monitoring Correlated Sources: AoI-based Scheduling is Nearly Optimal
topic Networking and Internet Architecture
Systems and Control
url https://arxiv.org/abs/2312.16813