Method for rapid deployment of low-cost sensors for a nationwide project in the Internet of things era: Air quality monitoring in Taiwan

Abstract

When the air quality problem of PM 2.5 first raised public attention and an emerging low-cost sensor technology appeared suitable as a monitoring measure for said problem, Taiwan’s Environmental Protection Administration devised a nationwide project involving large-scale sensor deployment for effective pollution monitoring and management. However, the conventional siting optimization methods were inadequate for deploying thousands of sensors. Therefore, this study develops a rapid deployment method. The current results may serve as a reference for the Taiwan government for use in the aforementioned nationwide project, which is an environmental Internet of things–based plan involving 10,200 sensors to be deployed throughout the country. The four monitoring targets are classified as types of industry, traffic areas, communities, and remoteness, and a three-phase implementation structure is devised in the method. The open-source geographic information system software named QGIS was used to implement the proposed method with relevant spatial data from local open-data resources, which generated new, necessary geographic features and estimated sensor deployment quantity in Taiwan. The deployment result of the 10,200 sensors is 4790 in the type of industry, 708 of the traffic area, 3935 of the communities, and 767 of remoteness. The proposed method could serve as a useful foundation for the sensor deployment of environmental Internet of things. Policymakers may apply this method to budget allocation or integrate this method alongside conventional siting methods for the modification of deployment results based on the local monitoring requirements.