Design and Implementation of Temperature and Humidity Monitoring System Using LPWAN Technology
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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 | 0.001 |
| 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 it