Abstract
The internet of things (IoT) is a computing paradigm that expands every day along with the number of devices connected to the network including the home environment, that is why transmit information safely and be able to use all the computational capacity of the devices that compose it to analyze the generated data and find relevant information is one of the great challenges that it is tried to solve under the computational architecture proposed in the present article. The architecture proposed was tested in a laboratory environment with kitchen products information and the data obtained demonstrated that the architecture can solve the safe transmission using MQTT, storage in non-relational database and analysis of information with deep learning.
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Acknowledgments
This project is part of my master’s thesis in computational engineering at the University of Caldas and has contributed to the inter-institutional project between the university of caldas, the national university of Colombia and the company Mabe with hermes code 36715 entitled “Computational prototype for the fusion and analysis of large volumes of data in IoT environments (Internet of Things) from Machine Learning techniques and secure architectures between sensors, to characterize the behavior and interaction of users in a Connected Home ecosystem”.
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Osorio, C.A.C., Ossa, L.F.C., Echeverry, G.A.I. (2022). Computational Architecture of IoT Data Analytics for Connected Home Based on Deep Learning. In: Corchado, J.M., Trabelsi, S. (eds) Sustainable Smart Cities and Territories. SSCTIC 2021. Lecture Notes in Networks and Systems, vol 253. Springer, Cham. https://doi.org/10.1007/978-3-030-78901-5_14
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DOI: https://doi.org/10.1007/978-3-030-78901-5_14
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