Design and Implementation of Temperature and Humidity Monitoring System Using LPWAN Technology
Bibliographic record
Abstract
Temperature and humidity monitoring is an integral aspect of human lives and has several applications ranging from greenhouses, laboratories, food industries, server rooms, data centers, and so on. However, the primary technologies that drive these systems suffer from numerous drawbacks such as deployment cost, coverage, and power consumption. This paper aims to employ new and affordable technology, namely low power wide area networks, to implement a temperature and humidity monitoring system (THMS) due to their low cost, low power consumption, and long range in data transmission. A LoRa-based THMS using a Raspberry Pi 3 B+ gateway with a single channel packet forwarder and an Arduino UNO end device with a DHT11 sensor was designed and implemented. After registering the gateway and the end device on The Things Network (TTN), temperature and humidity values of 22.0 and 33.0% were recorded by the Arduino serial monitor and the TTN application server. The above implementation clearly shows that sensor values can be effectively transmitted using LoRa and LoRaWAN over long distances with minimal power consumption.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| 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 | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".