A New CEP-based air quality prediction framework for fog based IoT

Metehan Guzel; Suat Ozdemir

doi:10.1109/ISNCC.2019.8909090

A New CEP-based air quality prediction framework for fog based IoT

Metehan Guzel
, Suat Ozdemir

Gazi University

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

12 Citations (Scopus)

Abstract

Air pollution (AP) is a major problem for public health. To reduce effect of AP, air quality monitoring stations are deployed world-wide. But in addition to monitoring, by predicting air pollution levels, peoples exposure to pollution can be further reduced. In this work, we firstly review air quality (AQ) prediction literature in an algorithmic point-of-view. Then we introduce a new AQ prediction framework. The proposed framework utilizes Complex Event Processing to process huge amount of data in near real time. Fog Computing is utilized to achieve scalability, extendibility and Software Defined Network utilized to enhance manageability of the network. In this paper, we explain the network architecture and methodology behind the framework. The proposed framework can operate in near real time and does not need any human assistance.

Original language	English
Title of host publication	2019 International Symposium on Networks, Computers and Communications, ISNCC 2019
Publisher	Institute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)	9781728112435
DOIs	https://doi.org/10.1109/ISNCC.2019.8909090
Publication status	Published - Jun 2019
Externally published	Yes
Event	2019 International Symposium on Networks, Computers and Communications, ISNCC 2019 - Istanbul, Turkey Duration: 18 Jun 2019 → 20 Jun 2019

Publication series

Name	2019 International Symposium on Networks, Computers and Communications, ISNCC 2019

Conference

Conference	2019 International Symposium on Networks, Computers and Communications, ISNCC 2019
Country/Territory	Turkey
City	Istanbul
Period	18/06/19 → 20/06/19

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

SDG 3 Good Health and Well-being

Keywords

Air quality prediction
Big data
Complex event processing
Fog computing
Internet of Things (IoT)
IoT data analysis
Software-defined networking

Access to Document

10.1109/ISNCC.2019.8909090

Cite this

Guzel, M., & Ozdemir, S. (2019). A New CEP-based air quality prediction framework for fog based IoT. In 2019 International Symposium on Networks, Computers and Communications, ISNCC 2019 Article 8909090 (2019 International Symposium on Networks, Computers and Communications, ISNCC 2019). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/ISNCC.2019.8909090

@inproceedings{748361a2365c40e8befce52a1b93665e,

title = "A New CEP-based air quality prediction framework for fog based IoT",

abstract = "Air pollution (AP) is a major problem for public health. To reduce effect of AP, air quality monitoring stations are deployed world-wide. But in addition to monitoring, by predicting air pollution levels, peoples exposure to pollution can be further reduced. In this work, we firstly review air quality (AQ) prediction literature in an algorithmic point-of-view. Then we introduce a new AQ prediction framework. The proposed framework utilizes Complex Event Processing to process huge amount of data in near real time. Fog Computing is utilized to achieve scalability, extendibility and Software Defined Network utilized to enhance manageability of the network. In this paper, we explain the network architecture and methodology behind the framework. The proposed framework can operate in near real time and does not need any human assistance.",

keywords = "Air quality prediction, Big data, Complex event processing, Fog computing, Internet of Things (IoT), IoT data analysis, Software-defined networking",

author = "Metehan Guzel and Suat Ozdemir",

note = "Publisher Copyright: {\textcopyright} 2019 IEEE.; 2019 International Symposium on Networks, Computers and Communications, ISNCC 2019 ; Conference date: 18-06-2019 Through 20-06-2019",

year = "2019",

month = jun,

doi = "10.1109/ISNCC.2019.8909090",

language = "English",

series = "2019 International Symposium on Networks, Computers and Communications, ISNCC 2019",

publisher = "Institute of Electrical and Electronics Engineers Inc.",

booktitle = "2019 International Symposium on Networks, Computers and Communications, ISNCC 2019",

address = "United States",

}

Guzel, M & Ozdemir, S 2019, A New CEP-based air quality prediction framework for fog based IoT. in 2019 International Symposium on Networks, Computers and Communications, ISNCC 2019., 8909090, 2019 International Symposium on Networks, Computers and Communications, ISNCC 2019, Institute of Electrical and Electronics Engineers Inc., 2019 International Symposium on Networks, Computers and Communications, ISNCC 2019, Istanbul, Turkey, 18/06/19. https://doi.org/10.1109/ISNCC.2019.8909090

A New CEP-based air quality prediction framework for fog based IoT. / Guzel, Metehan; Ozdemir, Suat.
2019 International Symposium on Networks, Computers and Communications, ISNCC 2019. Institute of Electrical and Electronics Engineers Inc., 2019. 8909090 (2019 International Symposium on Networks, Computers and Communications, ISNCC 2019).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

TY - GEN

T1 - A New CEP-based air quality prediction framework for fog based IoT

AU - Guzel, Metehan

AU - Ozdemir, Suat

PY - 2019/6

Y1 - 2019/6

N2 - Air pollution (AP) is a major problem for public health. To reduce effect of AP, air quality monitoring stations are deployed world-wide. But in addition to monitoring, by predicting air pollution levels, peoples exposure to pollution can be further reduced. In this work, we firstly review air quality (AQ) prediction literature in an algorithmic point-of-view. Then we introduce a new AQ prediction framework. The proposed framework utilizes Complex Event Processing to process huge amount of data in near real time. Fog Computing is utilized to achieve scalability, extendibility and Software Defined Network utilized to enhance manageability of the network. In this paper, we explain the network architecture and methodology behind the framework. The proposed framework can operate in near real time and does not need any human assistance.

AB - Air pollution (AP) is a major problem for public health. To reduce effect of AP, air quality monitoring stations are deployed world-wide. But in addition to monitoring, by predicting air pollution levels, peoples exposure to pollution can be further reduced. In this work, we firstly review air quality (AQ) prediction literature in an algorithmic point-of-view. Then we introduce a new AQ prediction framework. The proposed framework utilizes Complex Event Processing to process huge amount of data in near real time. Fog Computing is utilized to achieve scalability, extendibility and Software Defined Network utilized to enhance manageability of the network. In this paper, we explain the network architecture and methodology behind the framework. The proposed framework can operate in near real time and does not need any human assistance.

KW - Air quality prediction

KW - Big data

KW - Complex event processing

KW - Fog computing

KW - Internet of Things (IoT)

KW - IoT data analysis

KW - Software-defined networking

UR - https://www.scopus.com/pages/publications/85075931854

U2 - 10.1109/ISNCC.2019.8909090

DO - 10.1109/ISNCC.2019.8909090

M3 - Conference contribution

AN - SCOPUS:85075931854

T3 - 2019 International Symposium on Networks, Computers and Communications, ISNCC 2019

BT - 2019 International Symposium on Networks, Computers and Communications, ISNCC 2019

PB - Institute of Electrical and Electronics Engineers Inc.

T2 - 2019 International Symposium on Networks, Computers and Communications, ISNCC 2019

Y2 - 18 June 2019 through 20 June 2019

ER -

A New CEP-based air quality prediction framework for fog based IoT

Abstract

Publication series

Conference

UN SDGs

Keywords

Access to Document

Other files and links

Fingerprint

Cite this