Life Span Improvement of Bio Sensors Using Unsupervised Machine Learning for Wireless Body Area Sensor Network
Bibliographic record
Abstract
Wireless body area networks (WBAN) are a popular subfield of wireless sensor networks used for continuous patient monitoring.WBAN is a network of many sensor nodes fused in and around the body to detect a patient's physical and behavioral activities and periodically send data to the base station, which may lead to the degradation of the energy efficiency of Biosensors.The authors proposed energy-efficient clustering methods using unsupervised learning in the present study.Ten sensor nodes were deployed on various parts of the human body using the OMNET++ simulator for analyzing multiple parameters using a systematic or query-based approach.The clustering approach is finalized based on the cluster head and obstacles in the deployment area.By reducing the number of packets, reception, and transmission, the sensor nodes can be disseminated, which improves the biosensors' lifetime.The number of rounds and network lifetime was studied by changing biosensors' critical parameters like first node death.The outcome was compared with the existing clustering protocols and found that the proposed protocol has been observed to increase network life span compared to the existing approaches, which will help to design an intelligent health monitoring system.
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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 | 0.001 | 0.002 |
| 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 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".