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 language | English |
|---|---|
| Pages (from-to) | 2630-2642 |
| Number of pages | 13 |
| Journal | Telkomnika (Telecommunication Computing Electronics and Control) |
| Volume | 16 |
| Issue number | 6 |
| DOIs | |
| Publication status | Published - Dec 1 2018 |
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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 journal › Article
}
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 -