Please use this identifier to cite or link to this item: https://hdl.handle.net/20.500.11851/2664
Title: MIS-IoT: Modular Intelligent Server Based Internet of Things Framework with Big Data and Machine Learning
Authors: Önal, Aras Can
Sezer, Ömer Berat
Özbayoğlu, Murat
Doğdu, Erdoğan
Keywords: Internet of things
machine learning
big data analytics
anomaly detection
fault detection
Issue Date: Jan-2019
Publisher: Institute of Electrical and Electronics Engineers Inc.
Source: Onal, A. C., Sezer, O. B., Ozbayoglu, M., and Dogdu, E. (2018, December). MIS-IoT: Modular Intelligent Server Based Internet of Things Framework with Big Data and Machine Learning. In 2018 IEEE International Conference on Big Data (Big Data) (pp. 2270-2279). IEEE.
Abstract: Internet of Things world is getting bigger everyday with new developments in all fronts. The new IoT world requires better handling of big data and better usage with more intelligence integrated in all phases. Here we present MIS-IoT (Modular Intelligent Server Based Internet of Things Framework with Big Data and Machine Learning) framework, which is »modular» and therefore open for new extensions, »intelligent» by providing machine learning and deep learning methods on »big data» coming from IoT objects, »server-based» in a service-oriented way by offering services via standart Web protocols. We present an overview of the design and implementation details of MIS-IoT along with a case study evaluation of the system, showing the intelligence capabilities in anomaly detection over real-time weather data. © 2018 IEEE.
Description: 2018 IEEE International Conference on Big Data (2018: Seattle, WA)
URI: https://ieeexplore.ieee.org/document/8622247
https://hdl.handle.net/20.500.11851/2664
ISBN: 9.78154E+12
Appears in Collections:Bilgisayar Mühendisliği Bölümü / Department of Computer Engineering
Scopus İndeksli Yayınlar Koleksiyonu / Scopus Indexed Publications Collection
WoS İndeksli Yayınlar Koleksiyonu / WoS Indexed Publications Collection

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