Big data has a substantial role nowadays, and its importance has significantly increased over the last decade. Big data’s biggest advantages are providing knowledge, supporting the decision-making process, and improving the use of resources, services, and infrastructures. The potential of big data increases when we apply it in real-time by providing real-time analysis, predictions, and forecasts, among many other applications. Our goal with this article is to provide a viewpoint on how to build a system capable of processing big data in real-time, performing analysis, and applying algorithms. A system should be designed to handle vast amounts of data and provide valuable knowledge through analysis and algorithms. This article explores the current approaches and how they can be used for the real-time operations and predictions.

Intelligent agriculture in general, but especially when agricultural fields are very heterogeneous, requires a large number of sensors in order to obtain an effective control and thus increase productivity. This need becomes more evident in vineyards on the farms of the demarcated Douro region due to the specificities of the territory and the vineyards themselves. Thus, it is necessary to have low cost sensors which are, essentially, easy to install and maintain. In the present work, a node with these characteristics was developed, which, in addition, is low consumption and communicates wirelessly through a Long Rang Wide Area Network (LoRaWAN) network. To obtain an easy installation, a library of clusters was created for the LoRaWAN network and dedicated to sensors used in agriculture, especially those using an asynchronous serial protocol for intelligent sensors. Three nodes were developed and tested with sensors used in agriculture to measure several environmental parameters (soil and air temperature; wind speed, gust and direction; soil water content, water tension and electrical conductivity; solar radiation; precipitation; atmospheric and vapor pressure; relative humidity; and lightning strikes count). The three nodes send data to a server through an existing gateway on the farm. The data are decoded and sent to an Internet-of-Things analytics platform where it is aggregated, viewed and analyzed. Samples of the data collected are presented. The developed nodes are of small dimensions ( $85\times 65\times 35\mathrm{m}\mathrm{m}$ ), thus making them easy to handle and install. Energy consumption depends on the distance to the gateway, and the number and type of sensors connected to each node. In the implemented cases, the maximum consumption was ≈ $400\mathsf{\mu }\mathrm{A}$ . The development of a cluster based library makes the node plug-and-play. The developed nodes will be a great step forward for the use of wireless sensors in smart agriculture in Douro vineyards.

The effective use of time series data is crucial in business decision-making. Temporal data reveals temporal trends and patterns, enabling decision-makers to make informed decisions and prevent potential problems. However, missing values in time series data can interfere with the analysis and lead to inaccurate conclusions. Thus, our work proposes a Focalize K-NN method that leverages time series properties to perform missing data imputation. This approach shows the benefits of taking advantage of correlated features and temporal lags to improve the performance of the traditional K-NN imputer. A similar approach could be employed in other methods. We tested this approach with two datasets, various parameter and feature combinations, and observed that it is beneficial in scenarios with disjoint missing patterns. Our findings demonstrate the effectiveness of Focalize K-NN for imputing missing values in time series data. The more noticeable benefits of our methods occur when there is a high percentage of missing data. However, as the amount of missing data increases, so does the error.