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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.

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The lack of a Management Plane specification in the software-defined networking (SDN) is a fundamental problem for the integrated management of networks that follow this paradigm. Several solutions have addressed this problem, but they do not provide data models intended to support Fault, Configuration, Accounting, Performance, and Security (FCAPS) in SDN from a specialized Management Plane. In this paper, we introduce a technologyagnostic approach that comprises mainly a collection of YANG data models and their relationships. These models characterize SDN management and handle technology heterogeneity. To evaluate our approach, we developed the proof of concept of the YANG data models in an SDN scenario involving the configuration of devices from different vendors supporting diverse technologies. The evaluation results show the efficiency of our approach regarding response time, composition time, consumption time, and network traffic.