Fog Computing is the distributed computing layer that lies between the user and the cloud. A successful fog architecture reduces delay or latency and increases efficiency. This paper describes the development and implementation of a distributed computing architecture applied to an automation environment that uses Fog Computing as an intermediary with the cloud computing layer.

This study used a Raspberry Pi board connected to end control elements such as servomotors and relays, indicators, and thermal sensors.  All is controlled by an automation framework that receives orders from Siri and executes them through predetermined instructions. The cloud connection benefits from a reduced amount of data transmission because it only receives relevant information for analysis.

This paper establishes a frame of reference for developing and implementing a Fog Computing architecture for the effects of concept testing, with the hardware and software tools and with web management and abstract and intuitive resources that facilitate understanding, thereby obtaining new ideas and intellectual resources to produce alternatives for both hardware and software for the implementation of this new trend in the academic environment.

All the modules used to this effect fulfill the project’s proposed objectives. The result obtained is consistent with the findings of the literature. In general, it was observed that sensor data was processed before being transported to the IoT platform and that the data is independent. It is important to select hardware that enables interaction with the various software modules.

It is suggested to follow the research steps that integrate the various types of modules, such as communications, acquisition, processing, and viewing of data, user management, and expansion of the information security layer.

*Pinzón Castellanos, J., & Cadena Carter, M. A. (2020). Fog Computing en el contexto de Smart Home, asistente de voz y futuro de IoT. Revista Colombiana De Computación, 21(1), 6-12

*DOI: https://doi.org/10.29375/25392115.3894