A big data processing framework for self-healing internet of things applications

Name: A big data processing framework for self-healing internet of things applications
Author: Dundar, B., Astekin, Merve, Aktas, M. S.

İsim	A big data processing framework for self-healing internet of things applications
Yazar	Dundar, B., Astekin, Merve, Aktas, M. S.
Basım Tarihi:	2016
Basım Yeri	- IEEE
Konu	Big data, Internet of things, Self-healing systems, Predictive maintenance, Complex event processing
Tür	Belge
Dil	İngilizce
Dijital	Evet
Yazma	Hayır
Kütüphane:	Özyeğin Üniversitesi
Demirbaş Numarası	978-1-5090-4795-6
Kayıt Numarası	8e53f72b-91e6-4e30-9256-7b66fc704613
Tarih	2016
Notlar	Due to copyright restrictions, the access to the full text of this article is only available via subscription.
Örnek Metin	In this study, we introduce a big data processing framework that provides self-healing capability in the Internet of Things domain. We discuss the high-level architecture of this framework and its prototype implementation. To identify faulty conditions, we utilize a complex-event processing technique by applying a rule-based pattern-detection algorithm on the events generated real-time. For events, we use a descriptor metadata of the measurements (such as CPU usage, memory usage, bandwidth usage) taken from Internet of Things devices. To understand the usability and effectiveness of the proposed architecture, we test the prototype implementation for performance and scalability under increasing incoming message rates. The results are promising, because its processing overhead is negligible.
DOI	10.1109/SKG.2016.017

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A big data processing framework for self-healing internet of things applications

Yazar Dundar, B., Astekin, Merve, Aktas, M. S.

Basım Tarihi 2016

Basım Yeri - IEEE

Konu Big data, Internet of things, Self-healing systems, Predictive maintenance, Complex event processing

Tür Belge

Dil İngilizce

Dijital Evet

Yazma Hayır

Kütüphane Özyeğin Üniversitesi

Demirbaş Numarası 978-1-5090-4795-6

Kayıt Numarası 8e53f72b-91e6-4e30-9256-7b66fc704613

Tarih 2016

Notlar Due to copyright restrictions, the access to the full text of this article is only available via subscription.

Örnek Metin In this study, we introduce a big data processing framework that provides self-healing capability in the Internet of Things domain. We discuss the high-level architecture of this framework and its prototype implementation. To identify faulty conditions, we utilize a complex-event processing technique by applying a rule-based pattern-detection algorithm on the events generated real-time. For events, we use a descriptor metadata of the measurements (such as CPU usage, memory usage, bandwidth usage) taken from Internet of Things devices. To understand the usability and effectiveness of the proposed architecture, we test the prototype implementation for performance and scalability under increasing incoming message rates. The results are promising, because its processing overhead is negligible.

DOI 10.1109/SKG.2016.017