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O uso das Tecnologias de Informação e Comunicação (TICs) tornou-se essencial nas atividades das instituições de educação por meio do ensino remoto com a suspensão das aulas presenciais por causa da pandemia da Covid-19. Devido às dificuldades de acesso aos laboratórios tradicionais para realização de práticas pelos estudantes da educação básica, tornou-se essencial a utilização de laboratórios remotos e virtuais para realização de práticas, que podem ser acessados via internet independente da localização geográfica dos estudantes, uma série destes têm sido desenvolvidos por algumas instituições, destaca-se a Universidade Federal de Santa Catarina (UFSC) através do grupo de pesquisa do Laboratório de Experimentação Remota (RexLab). Esse novo contexto passou a exigir pesquisas sobre projetos voltados para integração das TICs na educação básica sob a perspectiva da formação humana integral. Este artigo tem como objetivo de relatar sobre experiências exitosas dos projetos voltados para uso de tecnologias educacionais que utilizam os laboratórios remotos e virtuais voltado para o ensino médio no intuito de aplicação de práticas de disciplinas das ciências aplicadas, desenvolvido pelo grupo de pesquisa do RExLab da UFSC com apoio do Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPQ).

Abstract-This article presents two proposals in order to solve the problem of choosing the best access network available in the environment where the user is located. One based on a combination of fuzzy logic technique with two decision-making methods, AHP (Analytic Hierarchy Process) and GRA (Grey Relation Analysis), and the other based only on fuzzy logic technique. In order to demonstrate the effectiveness of these proposals, they were compared with a third one, of the authors in [7], which uses a combination of AHP method with a cost function. The obtained results show that the two proposals presented in this paper are more efficient in sorting and selecting the best access network when compared to the third.

The Network Slice Selection Function (NSSF) in heterogeneous technology environments is a complex problem, which still does not have a fully acceptable solution. Thus, the implementation of new network selection strategies represents an important issue in development, mainly due to the growing demand for applications and scenarios involving 5G and future networks. This work presents an integrated solution for the NSSF problem, called the Network Slice Selection Function Decision-Aid Framework (NSSF DAF), which consists of a distributed solution in which a part is executed on the user’s equipment (for example, smartphones, Unmanned Aerial Vehicles, IoT brokers) functioning as a transparent service, and another at the Edge of the operator or service provider. It requires a low consumption of computing resources from mobile devices and offers complete independence from the network operator. For this purpose, protocols and software tools are used to classify slices, employing the following four multicriteria methods to aid decision making: VIKOR (Visekriterijumska Optimizacija i Kompromisno Resenje), COPRAS (Complex Proportional Assessment), TOPSIS (Technique for Order Preference by Similarity to Ideal Solution) and Promethee II (Preference Ranking Organization Method for Enrichment Evaluations). The general objective is to verify the similarity among these methods and applications to the slice classification and selection process, considering a specific scenario in the framework. It also uses machine learning through the K-means clustering algorithm, adopting a hybrid solution in the implementation and operation of the NSSF service in multi-domain slicing environments of heterogeneous mobile networks. Testbeds were conducted to validate the proposed framework, mapping the adequate quality of service requirements. The results indicate a real possibility of offering a complete solution to the NSSF problem that can be implemented in Edge, in Core, or even in the 5G Radio Base Station itself, without the incremental computational cost of the end user’s equipment, allowing for an adequate quality of experience.