Implementation of integration VaaMSN and SEMAR for wide coverage air quality monitoring

Yohanes Yohanie Fridelin Panduman, Adnan Rachmat Anom Besari, Sritrusta Sukaridhoto, Rizqi Putri Nourma Budiarti, Rahardhita Widyatra Sudibyo, Nobuo Funabiki

Research output: Contribution to journalArticle

Abstract

The current air quality monitoring system cannot cover a large area, not real-time and has not implemented big data analysis technology with high accuracy. The purpose of an integration Mobile Sensor Network and Internet of Things system is to build air quality monitoring system that able to monitor in wide coverage. This system consists of Vehicle as a Mobile Sensors Network (VaaMSN) as edge computing and Smart Environment Monitoring and Analytic in Real-time (SEMAR) cloud computing. VaaMSN is a package of air quality sensor, GPS, 4G Wi-Fi modem and single board computing. SEMAR cloud computing has a time-series database for real-time visualization, Big Data environment and analytics use the Support Vector Machines (SVM) and Decision Tree (DT) algorithm. The output from the system are maps, table, and graph visualization. The evaluation obtained from the experimental results shows that the accuracy of both algorithms reaches more than 90%. However, Mean Square Error (MSE) value of SVM algorithm about 0.03076293, but DT algorithm has 10x smaller MSE value than SVM algorithm.

Original languageEnglish
Pages (from-to)2630-2642
Number of pages13
JournalTelkomnika (Telecommunication Computing Electronics and Control)
Volume16
Issue number6
DOIs
Publication statusPublished - Dec 1 2018

Fingerprint

Air quality
Sensor networks
Wireless networks
Monitoring
Support vector machines
Cloud computing
Decision trees
Mean square error
Computer systems
Visualization
Wi-Fi
Modems
Global positioning system
Time series
Sensors
Big data

Keywords

  • Air quality monitoring
  • Internet of things
  • Real-time analytical
  • SEMAR
  • VaaMSN

ASJC Scopus subject areas

  • Electrical and Electronic Engineering

Cite this

Implementation of integration VaaMSN and SEMAR for wide coverage air quality monitoring. / Panduman, Yohanes Yohanie Fridelin; Besari, Adnan Rachmat Anom; Sukaridhoto, Sritrusta; Budiarti, Rizqi Putri Nourma; Sudibyo, Rahardhita Widyatra; Funabiki, Nobuo.

In: Telkomnika (Telecommunication Computing Electronics and Control), Vol. 16, No. 6, 01.12.2018, p. 2630-2642.

Research output: Contribution to journalArticle

Panduman, Yohanes Yohanie Fridelin ; Besari, Adnan Rachmat Anom ; Sukaridhoto, Sritrusta ; Budiarti, Rizqi Putri Nourma ; Sudibyo, Rahardhita Widyatra ; Funabiki, Nobuo. / Implementation of integration VaaMSN and SEMAR for wide coverage air quality monitoring. In: Telkomnika (Telecommunication Computing Electronics and Control). 2018 ; Vol. 16, No. 6. pp. 2630-2642.
@article{cf181e0c4a0940068ace1460f81057bc,
title = "Implementation of integration VaaMSN and SEMAR for wide coverage air quality monitoring",
abstract = "The current air quality monitoring system cannot cover a large area, not real-time and has not implemented big data analysis technology with high accuracy. The purpose of an integration Mobile Sensor Network and Internet of Things system is to build air quality monitoring system that able to monitor in wide coverage. This system consists of Vehicle as a Mobile Sensors Network (VaaMSN) as edge computing and Smart Environment Monitoring and Analytic in Real-time (SEMAR) cloud computing. VaaMSN is a package of air quality sensor, GPS, 4G Wi-Fi modem and single board computing. SEMAR cloud computing has a time-series database for real-time visualization, Big Data environment and analytics use the Support Vector Machines (SVM) and Decision Tree (DT) algorithm. The output from the system are maps, table, and graph visualization. The evaluation obtained from the experimental results shows that the accuracy of both algorithms reaches more than 90{\%}. However, Mean Square Error (MSE) value of SVM algorithm about 0.03076293, but DT algorithm has 10x smaller MSE value than SVM algorithm.",
keywords = "Air quality monitoring, Internet of things, Real-time analytical, SEMAR, VaaMSN",
author = "Panduman, {Yohanes Yohanie Fridelin} and Besari, {Adnan Rachmat Anom} and Sritrusta Sukaridhoto and Budiarti, {Rizqi Putri Nourma} and Sudibyo, {Rahardhita Widyatra} and Nobuo Funabiki",
year = "2018",
month = "12",
day = "1",
doi = "10.12928/TELKOMNIKA.v16i6.10152",
language = "English",
volume = "16",
pages = "2630--2642",
journal = "Telkomnika",
issn = "1693-6930",
publisher = "Institute of Advanced Engineering and Science (IAES)",
number = "6",

}

TY - JOUR

T1 - Implementation of integration VaaMSN and SEMAR for wide coverage air quality monitoring

AU - Panduman, Yohanes Yohanie Fridelin

AU - Besari, Adnan Rachmat Anom

AU - Sukaridhoto, Sritrusta

AU - Budiarti, Rizqi Putri Nourma

AU - Sudibyo, Rahardhita Widyatra

AU - Funabiki, Nobuo

PY - 2018/12/1

Y1 - 2018/12/1

N2 - The current air quality monitoring system cannot cover a large area, not real-time and has not implemented big data analysis technology with high accuracy. The purpose of an integration Mobile Sensor Network and Internet of Things system is to build air quality monitoring system that able to monitor in wide coverage. This system consists of Vehicle as a Mobile Sensors Network (VaaMSN) as edge computing and Smart Environment Monitoring and Analytic in Real-time (SEMAR) cloud computing. VaaMSN is a package of air quality sensor, GPS, 4G Wi-Fi modem and single board computing. SEMAR cloud computing has a time-series database for real-time visualization, Big Data environment and analytics use the Support Vector Machines (SVM) and Decision Tree (DT) algorithm. The output from the system are maps, table, and graph visualization. The evaluation obtained from the experimental results shows that the accuracy of both algorithms reaches more than 90%. However, Mean Square Error (MSE) value of SVM algorithm about 0.03076293, but DT algorithm has 10x smaller MSE value than SVM algorithm.

AB - The current air quality monitoring system cannot cover a large area, not real-time and has not implemented big data analysis technology with high accuracy. The purpose of an integration Mobile Sensor Network and Internet of Things system is to build air quality monitoring system that able to monitor in wide coverage. This system consists of Vehicle as a Mobile Sensors Network (VaaMSN) as edge computing and Smart Environment Monitoring and Analytic in Real-time (SEMAR) cloud computing. VaaMSN is a package of air quality sensor, GPS, 4G Wi-Fi modem and single board computing. SEMAR cloud computing has a time-series database for real-time visualization, Big Data environment and analytics use the Support Vector Machines (SVM) and Decision Tree (DT) algorithm. The output from the system are maps, table, and graph visualization. The evaluation obtained from the experimental results shows that the accuracy of both algorithms reaches more than 90%. However, Mean Square Error (MSE) value of SVM algorithm about 0.03076293, but DT algorithm has 10x smaller MSE value than SVM algorithm.

KW - Air quality monitoring

KW - Internet of things

KW - Real-time analytical

KW - SEMAR

KW - VaaMSN

UR - http://www.scopus.com/inward/record.url?scp=85058266506&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=85058266506&partnerID=8YFLogxK

U2 - 10.12928/TELKOMNIKA.v16i6.10152

DO - 10.12928/TELKOMNIKA.v16i6.10152

M3 - Article

VL - 16

SP - 2630

EP - 2642

JO - Telkomnika

JF - Telkomnika

SN - 1693-6930

IS - 6

ER -