The environment consists of the interaction between the physical, biotic, and anthropic means. As this interaction is dynamic, environmental characteristics tend to change naturally over time, requiring continuous monitoring. In this scenario, the internet of things (IoT), together with traditional sensor networks, allows for the monitoring of various environmental aspects such as air, water, atmospheric, and soil conditions, and sending data to different users and remote applications. This paper proposes a Standard-based Internet of Things Platform and Data Flow Modeling for Smart Environmental Monitoring. The platform consists of an IoT network based on the IEEE 1451 standard which has the network capable application processor (NCAP) node (coordinator) and multiple wireless transducers interface module (WTIM) nodes. A WTIM node consists of one or more transducers, a data transfer interface and a processing unit. Thus, with the developed network, it is possible to collect environmental data at different points within a city landscape, to perform analysis of the communication distance between the WTIM nodes, and monitor the number of bytes transferred according to each network node. In addition, a dynamic model of data flow is proposed where the performance of the NCAP and WTIM nodes are described through state variables, relating directly to the information exchange dynamics between the communicating nodes in the mesh network. The modeling results showed stability in the network. Such stability means that the network has capacity of preserve its flow of information, for a long period of time, without loss frames or packets due to congestion.

The Internet of Things (IoT) is based on objects or “things” that have the ability to communicate and transfer data. Due to the large number of connected objects and devices, there has been a rapid growth in the amount of data that are transferred over the Internet. To support this increase, the heterogeneity of devices and their geographical distributions, there is a need for IoT gateways that can cope with this demand. The SOFTWAY4IoT project, which was funded by the National Education and Research Network (RNP), has developed a software-defined and virtualized IoT gateway that supports multiple wireless communication technologies and fog/cloud environment integration. In this work, we propose a planning method that uses optimization models for the deployment of IoT gateways in smart campuses. The presented models aimed to quantify the minimum number of IoT gateways that is necessary to cover the desired area and their positions and to distribute IoT devices to the respective gateways. For this purpose, the communication technology range and the data link consumption were defined as the parameters for the optimization models. Three models are presented, which use LoRa, Wi-Fi, and BLE communication technologies. The gateway deployment problem was solved in two steps: first, the gateways were quantified using a linear programming model; second, the gateway positions and the distribution of IoT devices were calculated using the classical K-means clustering algorithm and the metaheuristic particle swarm optimization. Case studies and experiments were conducted at the Samambaia Campus of the Federal University of Goiás as an example. Finally, an analysis of the three models was performed, using metrics such as the silhouette coefficient. Non-parametric hypothesis tests were also applied to the performed experiments to verify that the proposed models did not produce results using the same population.

Support Vector Machines (SVMs) technique for achieving classifiers and regressors. However, to obtain models with high accuracy and low complexity, it is necessary to define the kernel parameters as well as the parameters of the training model, which are called hyperparameters. The challenge of defining the more suitable value to hyperparameters is called the Parameter Selection Problem (PSP). However, minimizing the complexity and maximizing the generalization capacity of the SVMs are conflicting criteria. Therefore, we propose the Nature Inspired Optimization Tools for SVMs (NIOTS) that offers a method to automate the search process for the best possible solution for the PSP, allowing the user to quickly obtain several sets of good solutions and choose the one most appropriate for his specific problem. • The PSP has been modeled as a Multiobjective Optimization Problem (MOP) with two objectives: (1) good precision and (2) low complexity (low number of support vectors). • The user can evaluate multiple solutions included in the Pareto front, in terms of precision and low complexity of the model. • Apart from the Adaptive Parameter with Mutant Tournament Multiobjective Differential Evolution (APMT-MODE), the user can choose other metaheuristics and also among several kernel options.

The combination of multispectral data and machine learning provides effective and flexible monitoring of the soil nutrient content, which consequently positively impacts plant productivity and food security, and ultimately promotes sustainable agricultural development overall. The aim of this study was to investigate the associations between spectral variables and soil physicochemical attributes, as well as to predict these attributes using spectral variables as inputs in machine learning models. One thousand soil samples were selected from agricultural areas 0–20 cm deep and collected from Northeast Mato Grosso do Sul state of Brazil. A total of 20 g of the dried and homogenized soil sample was added to the Petri dish to perform spectral measurements. Reflectance spectra were obtained by CROP CIRCLE ACS-470 using three spectral bands: green (532–550 nm), red (670–700 nm), and red-edge (730–760 nm). The models were developed with the aid of the Weka environment to predict the soil chemical attributes via the obtained dataset. The models tested were linear regression, random forest (RF), reptree M5P, multilayer preference neural network, and decision tree algorithms, with the correlation coefficient (r) and mean absolute error (MAE) used as accuracy parameters. According to our findings, sulfur exhibited a correlation greater than 0.6 and a reduced mean absolute error, with better performance for the M5P and RF algorithms. On the other hand, the macronutrients S, Ca, Mg, and K presented modest r values (approximately 0.3), indicating a moderate correlation with actual observations, which are not recommended for use in soil analysis. This soil analysis technique requires more refined correlation models for accurate prediction.