SATVAM: Toward an IoT Cyber-Infrastructure for Low-Cost Urban Air Quality Monitoring

Abstract

Air pollution is a public health emergency in large cities. The availability of commodity sensors and the advent of Internet of Things (IoT) enable the deployment of a city-wide network of 1000's of low-cost real-time air quality monitors to help manage this challenge. This needs to be supported by an IoT cyber-infrastructure for reliable and scalable data acquisition from the edge to the Cloud. The low accuracy of such sensors also motivates the need for data-driven calibration models that can accurately predict the science variables from the raw sensor signals. Here, we offer our experiences with designing and deploying such an IoT software platform and calibration models, and validate it through a pilot field deployment at two mega-cities, Delhi and Mumbai. Our edge data service is able to even-out the differential bandwidths from the sensing devices and to the Cloud repository, and recover from transient failures. Our analytical models reduce the errors of the sensors from a best-case of 63% using the factory baseline to as low as 21%, and substantially advances the state-of-the-art in this domain.