ReLeC‐MEO: Reinforcement Learning‐Based Clustering With Multi‐Objective Efficient Optimization for Energy‐Efficient IoT Networks
Bibliographic record
Abstract
ABSTRACT In response to the increasing need for energy‐efficient wireless sensor networks (WSNs) in the quickly evolving Internet of Things (IoT) arena, we introduce ReLeC‐MEO, a new protocol that combines the ReLeC clustering approach with multi‐objective efficient optimization. ReLeC‐MEO improves energy efficiency by using clustering based on reinforcement learning to optimize network design. By finding non‐dominated solutions on the Pareto front, multi‐objective optimization enhances this procedure even more and guarantees a just trade‐off between data transmission quality, energy consumption, and network lifetime. Numerous simulations verify that ReLeC‐MEO works noticeably better than current techniques. In comparison to baseline protocols, it specifically achieves a 42.9% reduction in latency, a 51.6% drop in energy consumption, and a 35% increase in throughput. It also outperforms the next best protocol by 20.4% in terms of network longevity.
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.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".