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Implementation of Digital Geotwin-Based Mobile Crowdsensing to Support Monitoring System in Smart City

Author

Listed:
  • Suhono H. Supangkat

    (Smart City and Community Innovation Center, Bandung Institute of Technology, Bandung 40132, Indonesia
    School of Electrical and Informatics Engineering, Bandung Institute of Technology, Bandung 40132, Indonesia)

  • Rohullah Ragajaya

    (Smart City and Community Innovation Center, Bandung Institute of Technology, Bandung 40132, Indonesia
    School of Electrical and Informatics Engineering, Bandung Institute of Technology, Bandung 40132, Indonesia)

  • Agustinus Bambang Setyadji

    (Faculty of Earth Science and Technology, Bandung Institute of Technology, Bandung 40132, Indonesia)

Abstract

According to the UN (United Nations) data released in 2018, the growth in the world’s population in urban areas is increasing every year. This encourages changes in cities that are increasingly dynamic in infrastructure development, which has an impact on social, economic, and environmental conditions. On the other hand, this also raises the potential for new problems in urban areas. To overcome potential problems that occur in urban areas, a smart, effective, and efficient urban monitoring system is needed. One solution that can be implemented is the Smart City concept which utilizes sensor technology, IoT, and Cloud Computing to monitor and obtain data on problems that occur in cities in real time. However, installing sensors and IoT throughout the city will take a long time and be relatively expensive. Therefore, in this study, it is proposed that the Mobile Crowdsensing (MCS) method is implemented to retrieve and collect data on problems that occur in urban areas from citizen reports using their mobile devices. MCS implementation in collecting data from the field is relatively inexpensive and does not take long because all data and information are sent from citizens or the community. The data and information that has been collected from the community are then integrated and visualized using the Digital Geotwin-based platform. Compared to other platforms, which are mostly still based on text and GIS in 2D, the advantage of Digital Geotwin is being able to represent and simulate real urban conditions in the physical world into a virtual world in 3D. Furthermore, the use of the Digital Geotwin-based platform is expected to improve the quality of planning and policy making for stakeholders. This research study aims to implement the MCS method in retrieving and collecting data in the form of objects and problem events from the field, which are then integrated into the Digital Geotwin-based platform. Data collected from MCS are coordinate data and images of problem objects. These are the contributions of this research study: the first is to increase the accuracy in determining the coordinates of a distant object by adding a parameter in the form of the approximate coordinates of the object. Second, 3D visualization of the problem object using image data obtained through the MCS method and then integrating it into the Digital Geotwin-based platform. The results of the research study show a fairly good increase in accuracy for determining the coordinates of distant objects. Evaluation results from the visualization of problem objects in 3D have also proven to increase public understanding and satisfaction in capturing information.

Suggested Citation

  • Suhono H. Supangkat & Rohullah Ragajaya & Agustinus Bambang Setyadji, 2023. "Implementation of Digital Geotwin-Based Mobile Crowdsensing to Support Monitoring System in Smart City," Sustainability, MDPI, vol. 15(5), pages 1-27, February.
    • Handle: RePEc:gam:jsusta:v:15:y:2023:i:5:p:3942-:d:1076083
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    References listed on IDEAS

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    1. Sumit Mishra & Devanjan Bhattacharya & Ankit Gupta, 2018. "Congestion Adaptive Traffic Light Control and Notification Architecture Using Google Maps APIs," Data, MDPI, vol. 3(4), pages 1-19, December.
    2. Ehab Shahat & Chang T. Hyun & Chunho Yeom, 2021. "City Digital Twin Potentials: A Review and Research Agenda," Sustainability, MDPI, vol. 13(6), pages 1-20, March.
    3. Changfeng Jing & Mingyi Du & Songnian Li & Siyuan Liu, 2019. "Geospatial Dashboards for Monitoring Smart City Performance," Sustainability, MDPI, vol. 11(20), pages 1-23, October.
    4. Cui, JianXun & Liu, Feng & Janssens, Davy & An, Shi & Wets, Geert & Cools, Mario, 2016. "Detecting urban road network accessibility problems using taxi GPS data," Journal of Transport Geography, Elsevier, vol. 51(C), pages 147-157.
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