IoT enabled Low power and Wide range WSN platform for environment monitoring application
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
Widely distributed and versatile nature of the environmental parameters makes it difficult to monitor consistently and continuously. Internet of Things (IoT) enabled wireless sensor networks (WSN) can make all these parameters visible to us and facilitate to understand the behavior of our environment. As both the IoT and WSN are of the distinct layered platform, we need to face technical difficulties while building an environment monitoring system using the available components. This paper proposed an easy to configure, low cost, low-power, and wide-range (LPWR) platform for environment monitoring applications. We implemented the sensor node using most of the available sensors required for environment monitoring application, used LoRa for a low-power and long-range WSN connectivity and the IoT for global visibility. We evaluated the field performance of the proposed platform in terms of sensor data consistency, data delivery success rate of the WSN for LoRa RSSI (Received Signal Strength Indicator), SNR (Signal to Noise Ratio) and the distance between the nodes. We also evaluated the WSN lifetime by measuring the energy consumption of the sensor node.
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 | 0.000 |
| 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 it