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 - Nobuo, Funabiki
N1 - Funding Information:
This research is funded by KEMERISTEKDIKTI from “Applied Leading Research in Higher Education (in Indonesia Penelitian Terapan Unggulan Perguruan Tinggi)” in 2018 scheme with the number: 09/PL14/PG.1/SP2P/2018 and the title is “Integrated as a Mobile Sensor Network Vehicle Implementation with Smart Environment Monitoring and Analytics in Real-time (SEMAR) system as Road Surface Monitoring and Environment to support Smart City (in Indonesia Implementation Vehicle as a Mobile Sensor Network terintegrasi dengan Smart Environment Monitoring and Analytics in Real-time (SEMAR) system sebagai Pemantauan
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
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U2 - 10.12928/TELKOMNIKA.v16i6.10152
DO - 10.12928/TELKOMNIKA.v16i6.10152
M3 - Article
AN - SCOPUS:85058266506
VL - 16
SP - 2630
EP - 2642
JO - Telkomnika
JF - Telkomnika
SN - 1693-6930
IS - 6
ER -