Abstract
The Internet of Things (IoT) is a technological paradigm in the sphere of networks that has the potential to influence how we live and how we work. This technology allows communication between all types of physical objects over the Internet that includes data sharing and also allows data to be collected and actions taken based on the information received. “Things” in the “Internet of Things” consist of a variety of hardware specifications, communication capabilities and service qualities, making IoT heterogeneous in its nature. The lack of Reference Modelling IoT Architecture prevents a common approach to processing the generated data. The need to retrieve and analyze this data from the Internet based complex systems in real-time requires applying of statistical data analysis and machine learning (ML) techniques as well as a sufficient amount of computational resources. In the paper a methodology to deal with a variety of data is proposed. A modular IoT system is considered as an instance for implementation of several methods for processing of heterogeneous data. The approaches for resolving problems that can affect the creation of predictive models are outlined.
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Dineva, K., Atanasova, T. (2019). Methodology for Data Processing in Modular IoT System. In: Vishnevskiy, V., Samouylov, K., Kozyrev, D. (eds) Distributed Computer and Communication Networks. DCCN 2019. Lecture Notes in Computer Science(), vol 11965. Springer, Cham. https://doi.org/10.1007/978-3-030-36614-8_35
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