An Approach Based on Fog Computing for Providing Reliability in IoT Data Collection: A Case Study in a Colombian Coffee Smart Farm
The reliability in data collection is essential in Smart Farming supported by the Internet of Things (IoT). Several IoT and Fog-based works consider the reliability concept, but they fall short in providing a network’s edge mechanisms for detecting and replacing outliers. Making decisions based on i...
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doaj-b33e28035e364a9c80c7cbba02f53a5f2020-12-15T00:00:15ZengMDPI AGApplied Sciences2076-34172020-12-01108904890410.3390/app10248904An Approach Based on Fog Computing for Providing Reliability in IoT Data Collection: A Case Study in a Colombian Coffee Smart FarmAna Isabel Montoya-Munoz0Oscar Mauricio Caicedo Rendon1Departamento de Telemática, Universidad del Cauca, Popayán, Cauca 190002, ColombiaDepartamento de Telemática, Universidad del Cauca, Popayán, Cauca 190002, ColombiaThe reliability in data collection is essential in Smart Farming supported by the Internet of Things (IoT). Several IoT and Fog-based works consider the reliability concept, but they fall short in providing a network’s edge mechanisms for detecting and replacing outliers. Making decisions based on inaccurate data can diminish the quality of crops and, consequently, lose money. This paper proposes an approach for providing reliable data collection, which focuses on outlier detection and treatment in IoT-based Smart Farming. Our proposal includes an architecture based on the continuum IoT-Fog-Cloud, which incorporates a mechanism based on Machine Learning to detect outliers and another based on interpolation for inferring data intended to replace outliers. We located the data cleaning at the Fog to Smart Farming applications functioning in the farm operate with reliable data. We evaluate our approach by carrying out a case study in a network based on the proposed architecture and deployed at a Colombian Coffee Smart Farm. Results show our mechanisms achieve high Accuracy, Precision, and Recall as well as low False Alarm Rate and Root Mean Squared Error when detecting and replacing outliers with inferred data. Considering the obtained results, we conclude that our approach provides reliable data collection in Smart Farming.https://www.mdpi.com/2076-3417/10/24/8904internet of thingsreliabilityoutliersfog computingSmart Farming |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Ana Isabel Montoya-Munoz Oscar Mauricio Caicedo Rendon |
spellingShingle |
Ana Isabel Montoya-Munoz Oscar Mauricio Caicedo Rendon An Approach Based on Fog Computing for Providing Reliability in IoT Data Collection: A Case Study in a Colombian Coffee Smart Farm Applied Sciences internet of things reliability outliers fog computing Smart Farming |
author_facet |
Ana Isabel Montoya-Munoz Oscar Mauricio Caicedo Rendon |
author_sort |
Ana Isabel Montoya-Munoz |
title |
An Approach Based on Fog Computing for Providing Reliability in IoT Data Collection: A Case Study in a Colombian Coffee Smart Farm |
title_short |
An Approach Based on Fog Computing for Providing Reliability in IoT Data Collection: A Case Study in a Colombian Coffee Smart Farm |
title_full |
An Approach Based on Fog Computing for Providing Reliability in IoT Data Collection: A Case Study in a Colombian Coffee Smart Farm |
title_fullStr |
An Approach Based on Fog Computing for Providing Reliability in IoT Data Collection: A Case Study in a Colombian Coffee Smart Farm |
title_full_unstemmed |
An Approach Based on Fog Computing for Providing Reliability in IoT Data Collection: A Case Study in a Colombian Coffee Smart Farm |
title_sort |
approach based on fog computing for providing reliability in iot data collection: a case study in a colombian coffee smart farm |
publisher |
MDPI AG |
series |
Applied Sciences |
issn |
2076-3417 |
publishDate |
2020-12-01 |
description |
The reliability in data collection is essential in Smart Farming supported by the Internet of Things (IoT). Several IoT and Fog-based works consider the reliability concept, but they fall short in providing a network’s edge mechanisms for detecting and replacing outliers. Making decisions based on inaccurate data can diminish the quality of crops and, consequently, lose money. This paper proposes an approach for providing reliable data collection, which focuses on outlier detection and treatment in IoT-based Smart Farming. Our proposal includes an architecture based on the continuum IoT-Fog-Cloud, which incorporates a mechanism based on Machine Learning to detect outliers and another based on interpolation for inferring data intended to replace outliers. We located the data cleaning at the Fog to Smart Farming applications functioning in the farm operate with reliable data. We evaluate our approach by carrying out a case study in a network based on the proposed architecture and deployed at a Colombian Coffee Smart Farm. Results show our mechanisms achieve high Accuracy, Precision, and Recall as well as low False Alarm Rate and Root Mean Squared Error when detecting and replacing outliers with inferred data. Considering the obtained results, we conclude that our approach provides reliable data collection in Smart Farming. |
topic |
internet of things reliability outliers fog computing Smart Farming |
url |
https://www.mdpi.com/2076-3417/10/24/8904 |
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