With the advance in the Internet of Things (IoT), new ways of acquiring, processing, and managing collected data from electronic devices are being developed to provide support for more complex systems. This process of transforming the acquired data from the physical world, through the sensors, into viable information on which the applications can make decisions upon, must consider the various implementation scenarios and the business and technical requirements, such as security, privacy, and interoperability between heterogeneous devices (which often communicate using different protocols and require a common vocabulary). With the increasing complexity of these requirements, it becomes urgent to develop an infrastructure to handle the associated processes and provide a middle ground layer on which the physical and digital world are connected and translated into each other. This software layer, or middleware, can be described as a hub and aims to fill the gap between devices and information systems. This work contributes with a study of mechanisms and methodologies for the collection of data, interoperability of systems and data filtering, to optimize and automate, using a lightweight approach, and the collection and pre-analysis of the data to be used by the various applications of the IoT systems, such as the SME manufacturing industries.