Resource Scheduling in LoRaWAN using Chaotic Grouper-Moray Eel Optimization Algorithm
Bibliographic record
Abstract
Long Range Wide Area Network (LoRaWAN) is employed in IoT applications because of its low power consumption and long-range communication capabilities. Still, efficient resource scheduling is a challenging aspect to enhance network performance and energy efficiency. This study introduces an optimal resource scheduling model for LoRaWAN using clustering and optimization techniques for handling large scale network applications. Initially, the Low-Energy Adaptive Clustering Hierarchy (LEACH) technique is employed to cluster the nodes for reducing energy consumption and improving communication efficiency. Then, the resource scheduling is employed using the proposed Chaotic Chebyshev Groupers-Moray Eel Optimization (ChGM) algorithm. The ChGM algorithm utilized chaotic mapping and evolutionary behaviors of groupers and moray eels to optimize scheduling decisions by considering the Packet Delivery Ratio (PDR) as the fitness criterion. The consideration of clustering with resource scheduling, the proposed model accomplished better PSR, PCR, Latency and Throughput of 97.848%, 3.209%, 15.474ms, and 97.842%in LoRaWAN-based IoT networks.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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".