Optimized Clustering and Routing for Energy Efficiency in Smart Agricultural Wireless Sensor Networks
Bibliographic record
Abstract
Innovative solutions for farming resource optimization, decision aid, and land monitoring are wireless sensor networks. Precision farming uses real-time field adjustments and crop health data to improve harvest efficiency. Spatially distributed WSNs (wireless sensor networks) will be used more in agriculture. Research has focused on en ergyefficiency techniques, which will become increasingly important as sensor nodes become more spread. Wireless communication may use plenty of energy. K-Medoid for clustering, Enhanced Artificial Bee Colony (EABC) for Adaptive Harris Hawk Optimization (AHHO) and cluster head selection for routing were developed for precision agriculture WSNs to save energy. Dispersed sensor nodes (DSN) This technique improves energy efficiency and network longevity works perfectly for dynamic agricultural and maximum-scale IoT deployments. Compared to CL-HPWSR, GCEEC, EW-DHOA, and LEACH, this algorithm is more reliable, scalable, and sustainable in smart agriculture applications. This improves farming efficiency and sustainability. Simulation results show that these methods achieves up to 23% lower energy consumption, 0.95 Mbps throughput (100 nodes), 98.7% packet delivery ratio, and 18$\mathbf{2 2 \%}$ longer network lifetime over existing approaches.
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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 | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| 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".