Research about recommendation systems has increased due to the amount of information that it is available to individuals. In the music context these systems help the individual to filter and discover new songs according the individual's taste. Most of the business music companies use a recommendation system, based on the characteristics of a song listened by an individual, but a group recommendation system is still underexplored. For a shared environment when there is music, the songs selection will be more efficient if a group recommendation system is used. The goal of this project is to develop a music recommendation for a group that, is sharing the same environment, taking into consideration the context. For this reason, in this work we will employ the Spotify API to recover the data of playlists that were listened by an individual, collecting its preferences and adding them to the others individuals playlists.
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Este trabalho apresenta o estudo da performance de algoritmos de criptografia em plataformas de Internet das Coisas. O presente trabalho tem como objetivo estudar o funcionamento de algoritmo de criptografia simétrico AES e assimétrico RSA e aplicá-los a ambientes de Internet das Coisas, para que se possa avaliar o impacto na performance dos mesmos. Assim como, aplicar algoritmos de criptografia na camada de rede, na tentativa de garantir a segurança dos dados trocados em um ambiente de Internet das Coisas. Através do estudo, foi verificado que algoritmos assimétricos possuem maior impacto na performance do dispositivo, pois se baseam em cálculos complexos. Com isso, foram escolhidas plataformas utilizadas em prototipagem para mensurar o impacto no processamento. Ao realizar os testes, foi possível provar o impacto na rede e ajudar, através dos dados coletados, a escolher o algoritmo que melhor se adequa ao ambiente de Internet das Coisas, assim como, às necessidades de segurança dos mesmos.
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