What is it about?

The data calibration mechanism of the sensor network is an important basis for its data usability. This paper presents the Taiwan AirBox Project as an exemplary case to discuss the topics of open data, value-added services, and open-joint calibration services; as well as how these services generate productive public-private partnerships.

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Why is it important?

With one overarching goal at its core, the AirBox team hopes to gather a variety of actors in the construction of the sensor network; and demand the government's air quality control strategies to conduct a bottom-up style of sensor data as a foundation. Formerly, only experts with professional field knowledge deployed the sensor networks. Today, AirBox has revealed the possibility of a more democratic way for sensor network deployment. The citizen can play a leading role in determining sensor deployment locations and have the ability to examine the effects of specific pollution sources on air quality by designing experimental and control groups. However, citizen-led deployment may fall short of standardized scientific sampling and even lead to significant misinformation, resulting in distrust of AirBox data by public entities (and environmental agencies in particular). Thus, the greatest challenge of the sensor network's deployment and operation is to ensure data quality while accommodating the autonomy of participating private entities. The AirBox project adopted a strategy of combining open-source hardware, flexible database API, multiple value-added data services, and open-joint calibration to enhance data quality gradually. In addition, the AirBox project demonstrates the feasibility of a democratized deployment strategy.

Perspectives

Sensor network's data calibration is not only essential for data accuracy but particularly for building social trust. In the case of AirBox, "Openness" serves as the foundation for mutual trust, communication, cooperation, and co-creation among stakeholders.

Ming Kuang Chung
Academia Sinica

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This page is a summary of: From Participatory Sensing to Public-Private Partnership, November 2022, ACM (Association for Computing Machinery),
DOI: 10.1145/3560905.3578264.
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