LAUSR

With the evolution of technology over the years, it has become possible to develop intelligent environments based on the concept of the Internet of Things, distributed systems, and machine learning. Such environments are infused with various solutions to solve user demands from services. One of these solutions is the Ubiquitous Privacy (UBIPRI) middleware, whose central concept is to maintain privacy in smart environments and to receive notifications as one of its services. However, this service is freely performed, disregarding the privacy that the environment employs. Another consideration is that, based on the researched related work, it was possible to identify that the authors do not use statistical hypothesis tests in their solutions developed in the presented context. This work proposes an architecture for notification management in smart environments, composed by a notification manager named Privacy Notification Manager (PRINM) to assign it to UBIPRI and to aim to perform experiments between classification algorithms to delimit which one is most feasible to implement in the PRINM decision-making mechanism. The experiments showed that the J48 algorithm obtained the best results compared to the other algorithms tested and compared.