skip to main content
10.1145/3560905.3578264acmconferencesArticle/Chapter ViewAbstractPublication PagessensysConference Proceedingsconference-collections
research-article

From Participatory Sensing to Public-Private Partnership: The Development of AirBox Project in Taiwan

Published:24 January 2023Publication History

ABSTRACT

A complete sensor network should include sensors, data processing, and data services. However, to establish the legitimacy of sensor data for urban governance, sensor networks should go beyond simple deployment of sensors in the built environment and strive for deeper integration of data services within civil society. This paper presents the Taiwan AirBox Project as an exemplary case of practical deployment of a sensor network to discuss the topics of open data, value-added services, and joint calibration services; as well as how these services generate productive public-private partnerships.

The AirBox project adopted a strategy of combining open-source hardware, flexible database API, multiple value-added data services, and open-joint calibration to gradually enhance the data quality. The results suggested that: 1. open hardware and open source software are keys to expanding the deployment of the sensor network; 2. open data and diverse value-added services enhance the public's environmental awareness and advocacy; 3. the open joint-calibration system helps connect government policy formulation with public environmental awareness.

In addition, the AirBox project demonstrates the feasibility of a democratized deployment strategy. "Openness" serves as the foundation for mutual trust, communication, cooperation, and co-creation among stakeholders involved in the deployment process.

References

  1. Han, M. J. N., and Kim, M. J. 2021. A critical review of the smart city in relation to citizen adoption towards sustainable smart living. Habitat International, 108, 102312.Google ScholarGoogle ScholarCross RefCross Ref
  2. He, S., Shin, H. S., Xu, S., and Tsourdos, A. 2020. Distributed estimation over a low-cost sensor network: A review of state-of-the-art. Information Fusion, 54, 21--43.Google ScholarGoogle ScholarCross RefCross Ref
  3. Chen, Y., and Han, D. 2018. Water quality monitoring in smart city: A pilot project. Automation in Construction, 89, 307--316.Google ScholarGoogle ScholarCross RefCross Ref
  4. Ramesh, M. V., Prabha, R., Thirugnanam, H., Devidas, A. R., Raj, D., Anand, S., and Pathinarupothi, R. K. 2020. Achieving sustainability through smart city applications: protocols, systems and solutions using IoT and wireless sensor network. CSI Transactions on ICT, 8(2), 213--230.Google ScholarGoogle ScholarCross RefCross Ref
  5. Wu, J., Ota, K., Dong, M., and Li, C. 2016. A hierarchical security framework for defending against sophisticated attacks on wireless sensor networks in smart cities. IEEE Access, 4, 416--424.Google ScholarGoogle ScholarCross RefCross Ref
  6. Yaqoob, I., Ahmed, E., Hashem, I. A. T., Ahmed, A. I. A., Gani, A., Imran, M., and Guizani, M. 2017. Internet of things architecture: Recent advances, taxonomy, requirements, and open challenges. IEEE wireless communications, 24(3), 10--16.Google ScholarGoogle Scholar
  7. Michalec, A. O., Hayes, E., and Longhurst, J. 2019. Building smart cities, the just way. A critical review of "smart" and "just" initiatives in Bristol, UK. Sustainable Cities and Society, 47, 101510.Google ScholarGoogle ScholarCross RefCross Ref
  8. Albino, V., Berardi, U., and Dangelico, R. M. 2015. Smart cities: Definitions, dimensions, performance, and initiatives. Journal of urban technology, 22(1), 3--21.Google ScholarGoogle ScholarCross RefCross Ref
  9. Jaime, J., Sousa, I., Queluz, M. P., and Rodrigues, A. 2018. Planning a smart city sensor network based on LoRaWAN Technology. In 2018 21st international symposium on wireless personal multimedia communications (WPMC). IEEE, 35--40.Google ScholarGoogle Scholar
  10. Perez-Castillo, R., Carretero, A. G., Caballero, I., Rodriguez, M., Piattini, M., Mate, A., and Lee, D. 2018. DAQUA-MASS: An ISO 8000-61 based data quality management methodology for sensor data. Sensors, 18(9), 3105.Google ScholarGoogle ScholarCross RefCross Ref
  11. Du, R., Santi, P., Xiao, M., Vasilakos, A. V., and Fischione, C. 2018. The sensable city: A survey on the deployment and management for smart city monitoring. IEEE Communications Surveys and Tutorials, 21(2), 1533--1560.Google ScholarGoogle ScholarCross RefCross Ref
  12. Zhao, Q., and Gurusamy, M. 2008. Lifetime maximization for connected target coverage in wireless sensor networks. ACM transactions on networking, 16(6), 1378--1391.Google ScholarGoogle Scholar
  13. Hero, A. O., and Cochran, D. 2011. Sensor management: Past, present, and future. IEEE Sensors Journal, 11(12), 3064--3075.Google ScholarGoogle ScholarCross RefCross Ref
  14. Chen, L. J., Ho, Y. H., Lee, H. C., Wu, H. C., Liu, H. M., Hsieh, H. H. and Lung, S. C. C. 2017. An open framework for participatory PM2. 5 monitoring in smart cities. IEEE Access, 5, 14441--14454.Google ScholarGoogle ScholarCross RefCross Ref
  15. Asorey-Cacheda, R., Garcia-Sanchez, A. J., Zúñiga-Cañón, C., and Garcia-Haro, J. 2018. Crowdsourcing Optimized Wireless Sensor Network Deployment in Smart Cities: A Keynote. In Ibero-American Congress of Smart Cities. Springer, Cham, 65--79.Google ScholarGoogle Scholar
  16. Mak, H. W. L., and Lam, Y. F. 2021. Comparative assessments and insights of data openness of 50 smart cities in air quality aspects. Sustainable Cities and Society, 69, 102868.Google ScholarGoogle ScholarCross RefCross Ref
  17. Marsal-Llacuna, M. L. 2016. City indicators on social sustainability as standardization technologies for smarter (citizen-centered) governance of cities. Social Indicators Research, 128(3), 1193--1216.Google ScholarGoogle ScholarCross RefCross Ref
  18. Macke, J., Casagrande, R. M., Sarate, J. A. R., and Silva, K. A. 2018. Smart city and quality of life: Citizens' perception in a Brazilian case study. Journal of cleaner production, 182, 717--726.Google ScholarGoogle ScholarCross RefCross Ref
  19. Echebarria, C., Barrutia, J. M., and Aguado-Moralejo, I. 2021. The Smart City journey: a systematic review and future research agenda. Innovation: The European Journal of Social Science Research, 34(2), 159--201.Google ScholarGoogle ScholarCross RefCross Ref
  20. Mahajan, S., Luo, C. H., Wu, D. Y., and Chen, L. J. 2021. From Do-It-Yourself (DIY) to Do-It-Together (DIT): Reflections on designing a citizen-driven air quality monitoring framework in Taiwan. Sustainable Cities and Society, 66, 102628.Google ScholarGoogle ScholarCross RefCross Ref
  21. Geertman, S., Ferreira, J., Goodspeed, R., and Stillwell, J. 2015. Introduction to planning support systems and smart cities. In [21] Geertman, S., Ferreira, J., Goodspeed, R., and Stillwell, J. (eds) Planning support systems and smart cities. Springer, Cham, 1--17.Google ScholarGoogle Scholar
  22. De Filippi, F., and Cocina, G.G. 2022. Digital Technologies to Encourage e-Participation in Urban Regeneration. In Urban Regeneration and Community Empowerment Through ICTs. Springer, Cham, 21--44.Google ScholarGoogle Scholar
  23. Chen, L. J., Ho, Y. H., Hsieh, H. H., Huang, S. T., Lee, H. C., and Mahajan, S. 2017. ADF: An anomaly detection framework for large-scale PM2. 5 sensing systems. IEEE Internet of Things Journal, 5(2), 559--570.Google ScholarGoogle ScholarCross RefCross Ref
  24. PM25 Open Data API. 2022. Retrieved August 24, 2022 from https://app.swaggerhub.com/apis-docs/I2875/PM25_Open_Data/1.0.0Google ScholarGoogle Scholar
  25. AirBox Dashboard. 2022. Retrieved August 24, 2022 from https://pm25.lass-net.org/grafana/d/airbox_dashboard_v3/airdata?orgId=2&var-source=AirBox&var-device_id=08BEAC02863AGoogle ScholarGoogle Scholar
  26. Lung, S. C. C., Hsiao, P. K., Wen, T. Y., Liu, C. H., Fu, C. B., and Cheng, Y. T. 2014. Variability of intra-urban exposure to particulate matter and CO from Asian-type community pollution sources. Atmospheric Environment, 83, 6--13.Google ScholarGoogle ScholarCross RefCross Ref
  27. Mahajan, S., Liu, H. M., Tsai, T. C., and Chen, L. J. 208. Improving the accuracy and efficiency of PM2. 5 forecast service using cluster-based hybrid neural network model. IEEE Access, 6, 19193--19204.Google ScholarGoogle Scholar
  28. Mahajan, S., Tang, Y. S., Wu, D. Y., Tsai, T. C., and Chen, L. J. 2019. Car: The clean air routing algorithm for path navigation with minimal pm2. 5 exposure on the move. IEEE Access, 7, 147373--147382.Google ScholarGoogle ScholarCross RefCross Ref
  29. AirBox Dynamic Calibration Framework (DCF) Model Status Report. 2022. Retrieved August 25, 2022 from https://pm25.lass-net.org/DCF/Google ScholarGoogle Scholar

Index Terms

  1. From Participatory Sensing to Public-Private Partnership: The Development of AirBox Project in Taiwan
          Index terms have been assigned to the content through auto-classification.

          Recommendations

          Comments

          Login options

          Check if you have access through your login credentials or your institution to get full access on this article.

          Sign in
          • Published in

            cover image ACM Conferences
            SenSys '22: Proceedings of the 20th ACM Conference on Embedded Networked Sensor Systems
            November 2022
            1280 pages
            ISBN:9781450398862
            DOI:10.1145/3560905

            Copyright © 2022 ACM

            Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

            Publisher

            Association for Computing Machinery

            New York, NY, United States

            Publication History

            • Published: 24 January 2023

            Permissions

            Request permissions about this article.

            Request Permissions

            Check for updates

            Qualifiers

            • research-article

            Acceptance Rates

            SenSys '22 Paper Acceptance Rate52of187submissions,28%Overall Acceptance Rate174of867submissions,20%
          • Article Metrics

            • Downloads (Last 12 months)48
            • Downloads (Last 6 weeks)3

            Other Metrics

          PDF Format

          View or Download as a PDF file.

          PDF

          eReader

          View online with eReader.

          eReader