A Low-Cost Smart Home Automation to Enhance Decision-Making based on Fog Computing and Computational Intelligence

This work proposes STORM, a solution for decision-making in a residential environment that combines fog computing and computational intelligence. In this scenario, STORm is able to collect, treat, disseminate, detect and control information generated from the sensor nodes to the decision-making process. With this in mind, STORm is based on the development of an ensemble of classifiers to enhance precision in the decision-making process, as well as on the use of the fog computing paradigm to manage and process the actions in the residence in real-time. The idea is to provide computational resources closer to the end-users, processes them locally before transmits them to the cloud. When compared with the classical approaches adopted in the literature for classification, the results show that, as well as providing a high degree of accuracy in the classification, the STORm maintains a high stability in the decision-making process.