SIRCo: uma arquitetura para Sensoriamento Inteligente de Rádios Cognitivos

The increased demand for using the Industrial, Scientific and Medical (ISM) unlicensed frequency spectrum has caused interference problems and lack of resource availability for wireless networks. Cognitive radio (CR) have emerged as an alternative to reduce interference and intelligently use the spectrum. Several protocols were proposed aiming to mitigate these problems, but most have not been implemented in real devices. This work presents an architecture for Intelligent Sensing for Cognitive Radios (ISCRa), and a spectrum decision model (SDM) based on Artificial Neural Networks (ANN), which uses as input a database with local spectrum behavior and a database with primary users information. For comparison, a spectrum decision model based on AHP, which employs advanced techniques in its spectrum decision method was implemented. Another spectrum decision model that considers only a physical parameter for channel classification was also implemented. Spectrum decision models evaluated, as well as ISCRa's architecture were developed in GNU-Radio framework and implemented on real nodes. Evaluation of SDMs considered metrics of: delivery rate, latency (Round Trip Time - RTT) and handoff. Experiments on real nodes showed that ISCRa architecture with ANN based SDM increased packet delivery rate and presented fewer frequency variation (handoff) while maintaining latency. Considering higher bandwidth as application's Quality of Service requirement, ANN-SDM obtained the best results when compared to other SDM for cognitive radio networks (CRN).

Determinação da densidade amostral ótima para a geração de mapas temáticos na cafeicultura de precisão

Among the crops commercially exploited in Brazil, the coffee has a great economic importance, especially in the states of Minas Gerais and Espirito Santo. In the search for higher yield and lower environmental impact, farmers and researchers seek to develop new technologies that result in greater efficiency in various production processes of the coffee. For this, the adoption of precision agriculture in the management of operations in coffee crops, called precision coffee, has shown results that justify its use, by identifying the spatial variability of several variables, allowing its localized management and in the proper intensity. Unlike conventional management that is based on the average of observations in an area, precision agriculture uses a more detailed sampling, based on a sampling grid, which allows to represent in greater detail the reality of farming. Many previous studies have identified the spatial variability of the production of coffee system variables, but without worrying about the quality of information obtained due to the sampling grid used as precision and accuracy. Given the above, the objective of this study was to evaluate the quality of four different sampling grids for different variables and three times, in order to identify the most appropriate grid for use in precision coffee. Also aimed to compare the results between the precision coffee and conventional, according to reference values.