A Hybrid Multi-Hop Clustering and Energy-Aware Routing Protocol for Efficient Resource Management in Renewable Energy Harvesting Wireless Sensor Networks
Bibliographic record
Abstract
Energy Harvesting Wireless Sensor Networks (EH-WSNs) main goal is to increase efficiency in settings where Energy Harvesting (EH) is restricted by environmental resources. To solve the drawbacks of conventional Wireless Sensor Networks (WSNs) routing methods that usually ignore EH, this work presents a multi-hop clustering and renewable energy-based routing protocol designed for EH-WSNs. The suggested method performs clustering both centralized and decentralized using energy circumstances and the quantity of captured energy. The protocol operates in three phases: cluster formation, data transmission, and centralized management. To evaluate the effectiveness of the proposed approach, we analyze three distinct scenarios with different settings. The findings show that the suggested approach greatly lowers the total network energy usage while allowing a higher number of nodes to stay operational. We find that our approach outperforms AEHAC, CRBS, HUCL, and EADUC in terms of average energy levels, overall efficiency, network stability, and number of live nodes during the simulation. The results taken together show that the suggested method continuously improves network efficiency and stability in all assessed situations.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".