In this work, we present an intelligent system developed for energy consumption distributed control and monitoring. It supports real time cloudbased data visualization of power profiles from different areas, so as to optimize overall power consumption. The local intelligent processing unit (LIPU) that control the different environments is described. The communication network model that allows connecting multiple LIPUs to apply power consumption policies defined by the organization is analyzed, and the unit's capabilities in relation to cloud connectivity and realtime processing are considered through a theoretical scalability study. Finally, we describe relevant implementation features in the context of "Facultad
In IoT applications, data capture in a sensor network can generate a large flow of information between the nodes and the cloud, affecting response times and device complexity but, above all, increasing costs. Fog computing refers to the use of pre-processing tools to improve local data management and communication with the cloud. This work presents an analysis of the features that platforms implementing fog computing solutions should have. Additionally, an experimental work integrating two specific platforms used for controlling devices in a sensor network, processing the generated data, and communicating with the cloud is presented.
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