Wireless Sensors Network for Monitoring Linear Infrastructures Using MQTT Protocol on Raspberry Pi With nRF24l01 and Node-Red
Bibliographic record
Abstract
Studies have been intensified over the past few years in wireless sensors network, which has become a fast emerging technology for many innovative applications related with monitoring.In this paper, we proposed a wireless sensors network using inexpensive nRF24l01 transceivers and Raspberry Pi to monitor linear infrastructures like pipelines, surfaces of buried optical fiber for bushfire or river beds for flood detection.We realized this network using a tree architecture, with three hierarchy levels which are: The base sensor node, the data relay node, the data dissemination node.Using the open source Arduino IDE software and Node-Red, we programmed the microcontrollers for the various nodes and ran the Network Control Center (NCC).The base nodes are placed in deep sleep mode, for energy saving reasons.Each of the payloads sent by the nodes is analyzed at the NCC, in case of an undesirable event, the base station generates an alert and sends through Twitter a message containing the address of the affected nodes.We got good results, the base nodes were placed 80m apart and we didn't lose any payload in our tests.Due to the low current draw of these nodes, the BSN is expected to operate on battery power within five years of battery life, as the microcontrollers and transceivers are placed in deep sleep most of the time.
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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 | 0.000 | 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 | 0.001 | 0.000 |
| Research integrity | 0.000 | 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".