Infraestructura local para espacios inteligentes: uso de docker y microservicios en el monitoreo de adultos mayores
Bibliographic record
Abstract
La automatización de entornos inteligentes mejora la seguridad y calidad de vida de personas vulnerables, como los adultos mayores. Sin embargo, muchas soluciones existentes dependen de la nube, lo que incrementa los costos y compromete la privacidad y la latencia. Este trabajo propone una arquitectura descentralizada basada en microservicios y contenedores Docker, diseñada para operar de forma local utilizando hardware de bajo costo como la Raspberry Pi. El sistema implementado permite la integración de sensores ESP32 para el monitoreo ambiental y un botón de emergencia para generar alertas en tiempo real a través de servicios como Telegram. Los datos se procesan mediante FastAPI y se almacenan en InfluxDB, garantizando autonomía y seguridad en la gestión de la información. Las pruebas en un entorno simulado demostraron una respuesta rápida, bajo consumo de recursos y recuperación automática ante fallos. La solución presentada es escalable, flexible y adaptable, ideal para aplicaciones en hogares o centros de asistencia, con posibilidades de expansión futura hacia inteligencia artificial y entornos reales.
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 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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".