Sensor-based Wastewater Monitoring Framework to Detect COVID-19
Bibliographic record
Abstract
This paper introduces a simple Wireless Sensor Network (WSN)-based framework that uses Proteus sensors of Libelium Smart Water Xtreme IoT platform to detect e-Coli in wastewater, uses an efficient priority-based routing protocol for timely notification of the detection e-Coli at the COVID-19 detection lab to identify the existence of SARS-CoV-2, the virus that currently causes the COVID-19 pandemic. These sensors use fluorescence to monitor coli forms in real-time, determining if the water is polluted and contaminated with SARS-CoV-2 once tested at the lab. The framework also includes an efficient Packet Priority Routing Protocol (PPRP) that prioritizes data packets transmission related to detecting COVID-19 over other data packets for timely and emergency measures. Simulation results show that the proposed PPRP routing protocol is more efficient in terms of end-to-end data transmission delay and network energy consumption than existing LEACH and CPWS protocols.
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 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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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 | 0.002 |
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 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".