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

EAGER：利用公民科学为可持续和互联社区提供智能水传感

• 批准号: 1637251
• 负责人: Dong Wang
• 金额: $ 25.2万
• 依托单位: University of Notre Dame
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2016
• 资助国家: 美国
• 起止时间: 2016-09-01 至 2019-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1637251&HistoricalAwards=false
• 关键词: EAGER; Smart; Water; Sensing; Sustainable

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.

1637251 Wang, Dong 该项目的总体目标是开发一种基于公民科学的智能水传感系统，通过使用消费者端测量的众感知水质数据来准确、高效地检测饮用水污染。在使用点监测饮用水质量对于让消费者了解水安全并促进决策过程至关重要，以最大限度地减少可持续社区的公共健康威胁。该项目的目标是：i）利用社区群智感知的集体力量，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，使人类既成为系统的传感器又成为系统的用户； iii) 通过公民科学整合教育和研究，以提高普通民众对水质和公共卫生的了解； 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成果，以实现更加可持续和互联的社区。在该项目中， PI 计划开发一种新的智能水传感 (SWS) 系统，以可靠地监测当地社区（印第安纳州格兰杰）的水污染水平，并开发一种新颖的群体传感数据分析引擎 (CDAE)，以解决数据可靠性和数据稀疏性的挑战使用人群感知数据。该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。鉴于之前的工作很少，拟议的 SWS 系统的开发是探索性的，但该项目的成功将有助于使众感成为可靠的替代方案，从而改变家庭饮用水质量监测过程。

项目成果

An online reinforcement learning approach to quality-cost-aware task allocation for multi-attribute social sensing

一种用于多属性社会感知的质量成本感知任务分配的在线强化学习方法

• DOI: 10.1016/j.pmcj.2019.101086
• 发表时间: 2019-11
• 期刊: Pervasive and Mobile Computing
• 影响因子: 4.3
• 作者: Zhang, Yang;Zhang, Daniel;Vance, Nathan;Wang, Dong
• 通讯作者: Wang, Dong