English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Zendy Plus

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Zendy Tools

Zendy Open

We study a remote monitoring system in which a mutually independent andheterogeneous collection of finite-state irreducible continuous time Markovchain (CTMC) based information sources is considered. In this system, a commonremote monitor queries the instantaneous states of the individual CTMCsaccording to a Poisson process with possibly different intensities across thesources, in order to maintain accurate estimates of the original sources.\color{black}Three information freshness models are considered to quantify theaccuracy 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 givensource. Using these expressions, optimum sampling rates for all sources areobtained so as to maximize the weighted sum freshness of the monitoring system,subject to an overall sampling rate constraint. This optimization problem leadsto a water-filling solution with quadratic worst case computational complexityin the number of information sources. Numerical examples are provided tovalidate the effectiveness of the optimum sampling policy in comparison toseveral baseline sampling policies.

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