Freshness-Aware Status Update Control in Energy-Constrained IoT Networks with Correlated Traffic

Description

This repository contains the analytical outputs, replication-level and summarized discrete-event simulation results, correlation-persistence sweep data, operating-region evidence, tail-feasibility calculations, figure-source files, and verification materials supporting the study “Freshness-Aware Status Update Control in Energy-Constrained IoT Networks with Correlated Traffic.” Version 2 replaces the preliminary V32 archive deposited in Version 1 with the final V33 reproducibility package synchronized with the submission manuscript. The updated archive incorporates the author-final Figure 1, harmonized TeX and DOCX captions, supplementary figure and table numbering, revised literature positioning, and expanded automated consistency checks. The archive includes a generative 26-state continuous-time Markov chain (CTMC) and 78-equation stochastic hybrid systems (SHS) endpoint solver, a 7,200-run high-replication battery-stability campaign with 2,000 block-bootstrap resamples, a separately recomputed 9,600-run design workflow following source reshaping, and a 540-run initialization and simulation-horizon audit. A reader-oriented data manifest maps the manuscript’s principal results, tables, and figures to their primary evidence files. All numerical data were synthetically generated from analytically specified stochastic models; no external observational or personal data were used. The included automated verifier checks numerical consistency, analytical anchors, TeX–DOCX synchronization, PDF fonts, file integrity, float citations, and package structure. Instructions for reproducing and verifying the reported results are provided in the archive.

Steps to reproduce

1. Download and extract `CN_AoI_Energy_V33_Mendeley_V2.zip`, and change to the extracted reproducibility-package directory. 2. Create a compatible Python environment and install the required packages: ```bash python -m pip install -r requirements.txt ``` Exact package versions used for the certified release are listed in `requirements-certified.txt`. 3. Verify the integrity and internal consistency of the deposited archive: ```bash sha256sum -c SHA256SUMS.txt PYTHONDONTWRITEBYTECODE=1 python verification/verify_v33.py ``` The certified archive should report `OVERALL: PASS (312/312)`, and all 163 files listed in `SHA256SUMS.txt` should pass the checksum test. 4. Reproduce the six exact CTMC–SHS endpoint solutions directly from the model transition and reset rules: ```bash python scripts/solve_v31_exact_endpoints.py ``` The solver constructs the 26-state CTMC and 78 first-moment SHS equations and should reproduce the stored endpoint anchors with a maximum difference of approximately `8.9e-16`. 5. Regenerate the high-replication numerical campaigns: ```bash python scripts/run_v31_design_campaigns.py ``` This command generates the 7,200-run battery-stability campaign, the 9,600-run workflow following source reshaping, the 540-run initialization and simulation-horizon audit, the associated summaries, and the 2,000-resample replication-block bootstrap results. Use the `--resume` option if a run is interrupted. 6. Restore the author-approved Figure 1 files and regenerate the numerical figures: ```bash python scripts/sync_figure01_v33.py python scripts/build_v31_new_figures.py ``` 7. Rebuild the manuscript PDF, if a compatible LaTeX installation is available: ```bash latexmk -pdf main.tex cd supplementary latexmk -pdf supplementary.tex ``` The main manuscript should contain 44 pages, and the Supplementary Material should contain 10 pages with figures numbered S1–S7 and tables numbered S1–S2. 8. Run the verifier again after regeneration: ```bash PYTHONDONTWRITEBYTECODE=1 python verification/verify_v33.py ``` The file `data/DATA_MANIFEST.csv` maps each principal manuscript result, table, and figure to its primary evidence file. All numerical data are synthetically generated from the analytically specified stochastic models; no external observational or personal data are required.

Institutions

• Jeonbuk National University

  Jeollabuk-do, Jeonju
• Sangji University

  Gangwon-do, Wŏnju

Categories

Funders

• National Research Foundation of Korea

  Ministry of Science and ICT

  Daejeon, Daejeon

  Grant ID: NRF-2022R1I1A3067543

Licence