Detecció i predicció d'anomalies en dispositius IoT en l'Edge computing
Nowadays, Internet of Things (IoT) devices can run machine learning (ML) models. Taking advantage of the computational power of these devices, the incorporation of a ML model to detect and predict anomalies in the data (time series) is intended. Data is collected real time by sensors connected to th...
| Autor: | |
|---|---|
| Tipo de recurso: | tesis de maestría |
| Fecha de publicación: | 2021 |
| País: | España |
| Institución: | Universitat Oberta de Catalunya (UOC) |
| Repositorio: | O2, repositorio institucional de la UOC |
| OAI Identifier: | oai:openaccess.uoc.edu:10609/127056 |
| Acceso en línea: | http://hdl.handle.net/10609/127056 |
| Access Level: | acceso abierto |
| Palabra clave: | predicció d'anomalies aprenentatge automàtic internet de les coses anomaly prediction machine learning Internet of things aprendizaje automático internet de las cosas predicción de anomalías Internet of things -- TFM Internet de les coses -- TFM Internet de las coses -- TFM |
| Sumario: | Nowadays, Internet of Things (IoT) devices can run machine learning (ML) models. Taking advantage of the computational power of these devices, the incorporation of a ML model to detect and predict anomalies in the data (time series) is intended. Data is collected real time by sensors connected to the device. Predicting and detecting anomalies within the IoT device can provide benefits such as reducing the sending of erroneous data to server, and so that saving on transmission as well as on the processing of these data in the cloud, and to make filtering of erroneous data. The field of the project is the environmental and focuses on measuring the air quality. Sensors will measure air particles and IoT devices will be managed through Particle platform (https://www.particle.io/) . Two types of sensors will be used, Particulate Matter Sensor SPS30 and Grove - Laser PM2.5 Dust Sensor, and several particle sizes will be measured. This work aims to develop an ML model for the detection and prediction of anomalous data captured by sensors connected to IoT devices, and run it within the IoT devices. |
|---|