I-Corps: Internet of things for Construction Safety

这个I-Corps项目更广泛的影响/商业潜力是开发一个系统，将物联网技术完全集成到建筑行业。美国建筑业是美国最大的市场之一，占美国经济的很大一部分。2016年，该部门占所有行业就业人数的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20年里，超过28，000名美国建筑工人死于工作。基于物联网的技术已被实施为满足建筑行业众多需求的解决方案，但仍存在许多障碍，阻碍了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）在建筑行业典型的恶劣环境中的一致操作，3）缺乏服务和远程环境固有的其他数据质量问题。这个I-Corps项目是基于开发一个可互操作的系统，该系统能够实现智能建筑工地的数据连接。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。该领域使用的最多的传感器是用于安全监控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实时工人生理监控系统。该技术将桥接传感器的输出，并应用人工智能和多路访问边缘计算来优化累积的数据流量输入。该奖项反映了NSF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.