MétaCan
Menu
Back to cohort

A Novel Whittle Index-Based Scheduling for Age of Information Minimization in IoT Networks

2025· article· W7117767531 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

Venuenot available
Typearticle
Language
FieldComputer Science
TopicAge of Information Optimization
Canadian institutionsConcordia University
Fundersnot available
KeywordsMQTTScheduling (production processes)Markov decision processJob shop schedulingQueueing theoryMinificationMarkov processInternet of ThingsServer

Abstract

fetched live from OpenAlex

The Message Queuing Telemetry Transport (MQTT) protocol has become a widely adopted standard in IoT systems due to its lightweight and energy-efficient publish/subscribe architecture. However, ensuring timely data delivery remains a challenge, especially for applications requiring fresh information. In this paper, we address this issue by integrating the Age of Information (AoI) metric into the MQTT framework. We formulate the MQTT-AoI optimization problem as a Markov Decision Process (MDP) and propose a novel Whittle index-based scheduling policy. Simulation results demonstrate that the proposed approach effectively minimizes AoI while satisfying resource constraints. Furthermore, we show that the dual variable converge, ensuring the algorithm reaches a feasible solution under the hard constraints. This work provides a practical solution for managing information freshness in MQTT-based IoT networks.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Methods · Consensus signal: Methods
Teacher disagreement score0.497
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.003
Science and technology studies0.0000.000
Scholarly communication0.0000.003
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.011
GPT teacher head0.240
Teacher spread0.229 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Quick stats

Citations0
Published2025
Admission routes1
Has abstractyes

Explore more

Same topicAge of Information OptimizationFrench-language works237,207