An Energy-Efficient LoRa IoT System for Water Monitoring: Lessons Learned and Use Cases
Bibliographic record
Abstract
With the growing impact of urbanization, indus-trialization, and climate change, monitoring water bodies like lakes, rivers, and reservoirs has become essential for detecting pollutants, managing resources, and preventing environmental hazards. Water monitoring is a critical process that involves continuously assessing water characteristics to ensure the safety and sustainability of water resources. We can develop detection and prediction systems by analyzing the collected data from water bodies over time. However, developing and deploying a reliable water monitoring framework must be investigated and researched. Therefore, this paper presents a water monitoring framework to address the power consumption challenge, which is one of the most important aspects of a remote monitoring system. We designed and implemented a real-time remote water monitoring framework to achieve cost and battery efficiency. We deployed our prototype in a real-world situation and discussed our challenges and limitations. Our system contains three main parts: first, an end device, which is in charge of collecting information from water and transferring the data to the land; second, a gateway placed on shore to connect the end devices to a centralized cloud server; and Finally, a cloud platform for gathering, storing and analyzing the sensor's information. Also, we provided a comprehensive study on power consumption and different end device architectures to reduce power consumption. Finally, we described different use cases of a water monitoring system using low-cost sensors.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".