An Approach Based on Fog Computing for Providing Reliability in IoT Data Collection: A Case Study in a Colombian Coffee Smart Farm

Abstract

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.

A Systematic Survey on the Role of Cloud, Fog, and Edge Computing Combination in Smart Agriculture

“…Finally, Ref. [ 86 ] proposed an architecture to monitor temperature, humidity, light intensity, and soil moisture in a coffee farm.…”

“…For example, Ref. [ 86 ] introduced a Fog-Cloud based architecture approach to provide data collection reliability in Things-based Coffee Smart Farming. Ref.…”

“…In particular, Ref. [ 66 ] considered barn sensors, agro-meteo station in crops, and cattle sensors as the IoT layer, and the authors of [ 86 ] included sensing systems such as temperature, humidity, light intensity, and soil moisture. Ref.…”

“… Fog layer: Ref. [ 86 ] proposed Fog hierarchical architecture where authors introduced two Fog layers with the components of Fog controllers in the first layer and Fog nodes as the second. Ref.…”

“…[ 66 ] considered Cloud applications and APIs as a Cloud layer, whereas Ref. [ 86 ] presented Data Centers, SaaS, PaaS, and IaaS in the Cloud layer. Refs.…”