Game Theory-Based Multi-Hop Routing Protocol with Metaheuristic Optimization-Based Clustering Process in WSN for Precision Agriculture
Bibliographic record
Abstract
In precision agriculture, a wireless sensor network (WSN) is employed to gather data pertaining to atmospheric conditions.WSN consists of sensor nodes installed at multiple points in a greenhouse for monitoring soil properties like moisture, pesticide levels, air temperature, humidity level and so on.Sensor nodes transmit the measured parametric digital information to a sink node.It further transmits the sensed data to a decision support system.The decision system uses a crop development model to effectively manage irrigation, fertilization, and climate control systems in a greenhouse.This allows for exact control over temperature and humidity levels.By using appropriate inputs, crops may be effectively managed, resulting in improved crop health and increased yield.Reliable transmission data is a crucial design objective for WSN in precision agriculture as the presence of foliage in the transmission channel causes significant attenuation of the radiated waves.Additionally, it may cause scattering and diffraction of signals as well.A dynamic data routing protocol selects the optimum data paths for node data transmission in a WSN.This paper presents a novel energy-efficient multi-hop data routing protocol for WSN precision agriculture applications.The proposed method is named Grey Wolf Optimized Coalitional Game Theory-based (GO_CGT) multi-hop routing protocol is adopted for selecting the required features to measure the importance of the corresponding fitness and dilate the feature map containing less information.Moreover, a rapid and highly which achieves 78.2% of PDR, 12% of energy consumption, 34.7% of end-to-end delay, and 247kbps of throughput.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 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".