MABs for Adaptive Resource Optimization in LoRa IoT Networks
Why this work is in the frame
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Bibliographic record
Abstract
This paper investigates Multi-Armed Bandit (MAB) algorithms for adapting the transmission parameters of a a 2.4GHz Long Range (LoRa) Internet of Things (IoT) network. The aim is to dynamically select the optimal transmission parameters to reduce energy use while ensuring reliable data transmission under dynamic operating conditions. For this, the most well-known MAB strategies: Upper Confidence Bound (UCB), Exponential weights for exploration and exploitation (EXP3), Epsilon-Greedy, Thompson Sampling (TS), and Tug-of-War (ToW), are thoroughly evaluated using a reward function that balances energy efficiency, data reliability, and data rate. Our simulation results indicate that UCB consistently achieves the best compromise between computational complexity and transmission performance in all considered dynamic LoRa IoT settings, including stochastic and nonstationary ones.
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Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it