An Adaptive Power Allocation Scheme for Wildfire Emergency Communication Networks
Bibliographic record
Abstract
Emergency communication networks have gathered significant attention in recent years due to their high reliability and efficiency for disaster relief and rescue operations. Motivated by this need, this paper investigates emergency communication networks for wildfire rescue operations, utilizing long-range (LoRa) communication technology. Since the fluctuating humidity and temperature in wildfire rescue operations may affect the stability of communication links, the resulting disconnections can result in time delays in rescue operations and lost lives. To address these challenges, we formulate two real-time power allocation problems designed for two distinct scenarios: 1) Surrounded by wildfire, and 2) After the wildfire has subsided. These formulations account for varying surrounding temperatures and humidity levels, essential for maintaining stable communication links and prolonging overall network lifetime. A novel adaptive decentralized stochastic gradient tracking (ADSGT) scheme is proposed, which integrates decentralized gradient tracking with distributed averaging schemes. This scheme is specifically designed to achieve faster convergence rates and reduce overall energy consumption, thereby enhancing the reliability and efficiency of wildfire rescue operations. Extensive simulation results based on real-world wildfire rescue data show that the proposed ADSGT scheme significantly outperforms existing decentralized optimization schemes in terms of convergence rate and energy efficiency.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".