The fourth industrial revolution, also called Industry 4.0 is increasingly integrating the physical world with the digital world through the use of cyberphysical systems in industrial production plants. The development of this type of system uses the Internet of Things to enable equipment to exchange information with each other and their surroundings, thus constituting an intelligent industrial environment. Among many concepts that are emerging due to the fourth industrial revolution, one that can be highlighted is Predictive Maintenance, which seeks to predict when a machine will fail based on signals previously emitted by it. Considering the relevance of Predictive Maintenance using IoT sensors in the context of Industry 4.0, this work carried out a research in the areas of IoT and Fog Computing, which resulted in the development of a low cost architecture to collect data related to equipment operation in industries. Sensors were developed to collect machine vibration data, with configurable measurement periodicity, allowing them to maximize machine lifetime while maintaining the behavior of the collected data. To validate the proposed model, tests were performed with and without the use of the Fog Layer, with the use of different frequencies in the data collection and with or without the application of a data compression algorithm. The results did not prove that the use of a Fog Layer had a significant impact on reducing energy consumption. However, in tests with different frequencies in data collection, a relationship could be verified between the amount of data transmitted with the energy spent on operations
