Abstract
Smart bands are wearable devices that are frequently used in monitoring people’s activity, fitness, and health state. They can be also used in early detection of possibly dangerous health-related problems. The increasing number of wearable devices frequently transmitting data to scalable monitoring centers located in the Cloud may raise the Big Data challenge and cause network congestion. In this paper, we focus on the storage space consumed while monitoring people with smart IoT devices and performing classification of their health state and detecting possibly dangerous situations with the use of machine learning models in the Cloud and on the Edge. We also test two different repositories for storing sensor data in the Cloud monitoring center – a relational Azure SQL Database and the Cosmos DB document store.
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Acknowledgments
This work was supported by Microsoft Research within Microsoft Azure for Research Award grant, pro-quality grant for highly scored publications or issued patents of the Rector of the Silesian University of Technology, Gliwice, Poland (grant No 02/020/RGJ19/0167), and partially, by Statutory Research funds of Institute of Informatics, Silesian University of Technology, Gliwice, Poland (grant No BK/204/RAU2/2019).
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Gołosz, M., Mrozek, D. (2019). Detection of Dangers in Human Health with IoT Devices in the Cloud and on the Edge. In: Kozielski, S., Mrozek, D., Kasprowski, P., Małysiak-Mrozek, B., Kostrzewa, D. (eds) Beyond Databases, Architectures and Structures. Paving the Road to Smart Data Processing and Analysis. BDAS 2019. Communications in Computer and Information Science, vol 1018. Springer, Cham. https://doi.org/10.1007/978-3-030-19093-4_4
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DOI: https://doi.org/10.1007/978-3-030-19093-4_4
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