SATVAM: Streaming Analytics Over Temporal Variables From Air Quality Monitoring
Key Projects: Influencing Policy-Level Decisions
SATVAM : Streaming Analytics Over Temporal Variables From air quality monitoring

Ambient air quality Monitoring over Rural areas using Indigenous Technology


The SATVAM (Streaming Analytics over Temporal Variables from Air quality Monitoring) initiative has been developing low-cost air quality (LCAQ) sensor networks based on highly portable IoT software platforms. These LCAQ devices include PM2.5 as well as gas sensors. A unique feature of this low-cost sensor deployment was a swap-out experiment wherein four of the six sensors were relocated to different sites in the two phases. The swap-out experiment was crucial in investigating the efficacy of calibration models when applied to weather and air quality conditions vastly different from those present during calibration. A novel local calibration algorithm was developed based on metric learning that offers stable and accurate calibration performance.

100 low-cost sensors
Delhi, Mumbai, Lucknow, Guwahati, Jaipur, Chennai, Kanyakumari
5 years (2017-2022)
IIT Kanpur, IIT Bombay, Dukes University, Respirer Living Sciences & Intel, Department of Science & Technology, Govt of India, Indo-US Science and Technology Forum