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

Title	A big data processing framework for self-healing internet of things applications
Author	Dundar, B., Astekin, Merve, Aktas, M. S.
Publication Date:	2016
Publication Place	- IEEE
Subject	Big data, Internet of things, Self-healing systems, Predictive maintenance, Complex event processing
Type	Document
Language	English
Digital	Yes
Manuscript	No
Library:	Özyeğin University
Library Asset ID	978-1-5090-4795-6
Record ID	8e53f72b-91e6-4e30-9256-7b66fc704613
Date	2016
Notes	Due to copyright restrictions, the access to the full text of this article is only available via subscription.
Sample Text	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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Özyeğin University

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

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

Publication Date 2016

Publication Place - IEEE

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

Type Document

Language English

Digital Yes

Manuscript No

Library Özyeğin University

Library Asset ID 978-1-5090-4795-6

Record ID 8e53f72b-91e6-4e30-9256-7b66fc704613

Date 2016

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

Sample Text 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