I-Corps: Internet of things for Construction Safety

I-Corps项目更广泛的影响/商业潜力是开发一个将物联网技术完全集成到建筑行业的系统。美国建筑业是美国最大的市场之一，在美国经济中占很大一部分。2016年，该行业占整个行业就业的6.1%，占国内生产总值的4.2%。然而，建筑业是生产力最低和最危险的行业之一。在过去的二十年里，超过28,000名美国建筑工人在工作中死亡。基于物联网的技术已经作为一种解决方案被实施，以满足建筑行业的许多需求，但也有许多障碍，禁止这些技术的全面实施。该团队已经确定了多个部署挑战，包括1)物联网组件的互操作性，2)建筑行业典型恶劣环境下的一致性操作，以及3)缺乏服务和远程环境固有的其他数据质量问题。I-Corps项目的基础是开发一个可互操作的系统，使智能建筑工地的数据连接成为可能。建筑工地一直在迅速实施基于物联网的技术，以帮助工人的安全和生产力。该系统将这些设备和数据流联网，以实现最佳使用。现场使用最多的传感器是用于安全监测和危险检测的基于视觉的系统，以及用于防止疲劳和中暑的工人实时生理监测系统。该技术将桥接传感器的输出，并应用人工智能和多访问边缘计算来优化累积的数据流量的输入。该奖项反映了美国国家科学基金会的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。

The broader impact/commercial potential of this I-Corps project is the development of a system that fully integrates internet-of-things technology in the construction industry. The U.S. construction industry is one of the largest markets in the nation and makes up a substantial portion of the U.S. economy. In 2016, this sector attributed 6.1 percent of all industry employment and 4.2 percent of the gross domestic product. However, the construction industry is one of the least productive and most dangerous industries. In the past two decades, more than 28,000 U.S. construction workers have died at work. Internet-of-things-based technologies have been implemented as a solution to meet the many needs of the construction industry, but there have been many barriers that prohibit the full implementation of these technologies. The team has identified multiple deployment challenges, including 1) interoperability of internet-of-things components, 2) consistent operation within harsh environments typical of the construction industry, and 3) lack of service and other data quality issues inherent to remote environments.This I-Corps project is based on the development of an interoperable system that enables the data connectivity of an intelligent construction jobsite. Construction sites have been quickly implementing internet of things-based technologies to aid in the safety and productivity of their workers. This system networks these devices and data streams for optimal use. The most numerous sensors used in the field are vision-based systems for safety monitoring and hazard detection, and real-time worker physiological monitoring systems to prevent fatigue and heatstroke. This technology will bridge the output from the sensors and apply artificial intelligence and multi-access edge computing to optimize the input of data traffic that accumulates.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.