The overall goal of this project is to develop a citizen science based smart water sensing system that accurately and efficiently detects drinking water contamination by using crowdsensing water quality data measured at the consumers' end. Monitoring drinking water quality at the point of use is vitally important to inform consumers about the water safety and to facilitate the decision-making process to minimize public health threats for a sustainable community. This project targets to: i) provide a brand new and transformative drinking water monitoring system by leveraging the collective power of crowdsensing in a community; ii) address fundamental challenges in crowdsensing and enable humans to be both sensors and users of the system; iii) integrate education and research through citizen science to enhance knowledge of common people on water quality and public health; and iv) engage government officials and residents (end users) throughout the process to address a real-world problem in a local community, and generate outcomes that will be broadly applicable in other places to enable more sustainable and connected communities.
In this project, the PIs plan to develop a new Smart Water Sensing (SWS) system to reliably monitor the water contamination levels in a local community (Granger, IN) and a novel Crowdsensing Data Analysis Engine (CDAE) to address the data reliability and data sparsity challenges of using crowdsensing data. The research is a novel combination of two distinct disciplines: computer science and environmental engineering. The development of the proposed SWS system is exploratory given little prior work, but the success of this project would help to make crowdsensing a reliable alternative that transforms the household drinking water quality monitoring process.