EAGER: Smart Water Sensing for Sustainable and Connected Communities Using Citizen Science

该项目的总体目标是开发一个基于公民科学的智能水传感系统，该系统通过在消费者端测量的众测水质数据，准确有效地检测饮用水污染。在使用地点监测饮用水质量对于向消费者通报水的安全情况和促进决策进程以尽量减少对可持续社区的公共健康威胁至关重要。这个项目的目标是：i)通过在社区中利用群体感知的集体力量，提供一个全新的、变革性的饮用水监测系统；Ii)解决群体感知的基本挑战，使人类同时成为系统的传感器和用户；（三）通过公民科学将教育和研究结合起来，提高普通民众对水质和公共卫生的认识；iv)让政府官员和居民（最终用户）参与整个过程，以解决当地社区的现实问题，并产生可广泛适用于其他地方的结果，以实现更可持续和更紧密的社区。在该项目中，pi计划开发一种新的智能水感测（SWS）系统，以可靠地监测当地社区的水污染水平（Granger, In），并开发一种新的众感数据分析引擎（CDAE），以解决使用众感数据的数据可靠性和数据稀疏性挑战。这项研究是计算机科学和环境工程这两个不同学科的新颖结合。由于之前的工作很少，所提议的SWS系统的开发是探索性的，但这个项目的成功将有助于使众感成为一种可靠的替代方案，改变家庭饮用水质量监测过程。

1637251 Wang, DongThe 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.