Query-Based Sampling of Heterogeneous CTMCs: Modeling and Optimization With Binary Freshness

We study a remote monitoring system in which a mutually independent and heterogeneous collection of finite-state irreducible continuous time Markov chain (CTMC) based information sources is considered. In this system, a common remote monitor queries the instantaneous states of the individual CTMCs a...

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Bibliographic Details
Published inIEEE transactions on communications Vol. 72; no. 12; pp. 7705 - 7714
Main Authors Akar, Nail, Ulukus, Sennur
Format Journal Article
LanguageEnglish
Published New York IEEE 01.12.2024
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects
Closed-form solutions
Continuous time systems
Estimates
Freshness
information freshness
Information sources
Markov chains
Markov information sources
Measurement
Monitoring
Monitoring systems
Optimization
optimum sampling
Remote monitoring
Sampling
Synchronization
Temperature measurement
Temperature sensors
water-filling based optimization
Online AccessGet full text
ISSN0090-6778
1558-0857
DOI10.1109/TCOMM.2024.3409542

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Summary:We study a remote monitoring system in which a mutually independent and heterogeneous collection of finite-state irreducible continuous time Markov chain (CTMC) based information sources is considered. In this system, a common remote monitor queries the instantaneous states of the individual CTMCs according to a Poisson process with possibly different intensities across the sources, in order to maintain accurate estimates of the original sources. Three information freshness models are considered to quantify the accuracy of the remote estimates: fresh when equal (FWE), fresh when sampled (FWS) and fresh when close (FWC). For each of these freshness models, closed-form expressions are derived for mean information freshness for a given source. Using these expressions, optimum sampling rates for all sources are obtained so as to maximize the weighted sum freshness of the monitoring system, subject to an overall sampling rate constraint. This optimization problem leads to a water-filling solution with quadratic worst case computational complexity in the number of information sources. Numerical examples are provided to validate the effectiveness of the optimum sampling policy in comparison to several baseline sampling policies.
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ISSN:0090-6778
1558-0857
DOI:10.1109/TCOMM.2024.3409542