Energy Efficient Exponentially Weighted Algorithm - Based Resource Allocation in LoRa Networks
Bibliographic record
Abstract
Low-power wide-area networks (LPWANs) increasingly attract attention in the IoT community. The provision of long communication ranges with low energy consumption is the main reason behind LPWAN’s growing popularity. Energy efficiency is crucial for LPWAN devices that are mostly battery-powered and required to function in a crowded environment. To reduce energy consumption over these networks, minimizing the collision rate in the packet transmission process is one of the possible solutions. However, existing radio resource allocation management algorithms do not fulfill the energy efficiency required by IoT devices. We propose Energy Efficient Exponentially Weighted Algorithm Based Resource Allocation, which considers each packet’s energy consumption level and transmission time in learning the best set of resources to be allocated to each end-device. We achieve 30% lower energy consumption per packet transmission than the baseline methods, which is noticeable when considering the whole network packet transmission, at the expense of losing 2% of the successful transmission rate.
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How this classification was reachedexpand
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 | 0.000 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".