Comparative Analysis of Scalable IoT Topologies for Optimal and Precise Greenhouse Environment Monitoring
Bibliographic record
Abstract
Precision agriculture is vital for optimizing parameters for plant growth and health particularly in controlled environments like greenhouses. The Internet of Things (IoT) is a driver of this, and Wireless Sensor Networks provide a structured approach for data acquisition in these environments. While WSNs are widely implemented, there is limited empirical comparison of signal performance metrics across different sensor motes. This study presents a comparative analysis using the Received Signal Strength Indicator (RSSI) parameter of two different IoT devices, Iris Mote and Zolertia RE-Mote in a greenhouse environment, specifically at the K.C. Irving Environmental Science Centre Greenhouse at Acadia University. Results indicate that the Zolertia RE-Mote offers superior range, processing power, and energy efficiency, making it better suited for scalable deployments. These findings highlight the importance of environment specific testing for WSN deployment in precision agriculture.
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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 | 0.003 |
| 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 | 0.000 | 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".