RCN: SAVI: Adaptive Management and Use of Resilient Infrastructures in Smart Cities: Support for Global Collaborative Research on Real-Time Analytics of Heterogeneous Big Data

RCN：SAVI：智慧城市弹性基础设施的适应性管理和使用：支持异构大数据实时分析的全球协作研究

• 批准号: 1550379
• 负责人: Calton Pu
• 金额: $ 37.53万
• 依托单位: Georgia Tech Research Corporation
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2015
• 资助国家: 美国
• 起止时间: 2015-09-15 至 2021-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1550379&HistoricalAwards=false
• 关键词: RCN; SAVI; Adaptive; Management; Use

Cities provide ready and efficient access to facilities and amenities through shared civil infrastructures such as transportation and healthcare. Making such critical infrastructures resilient to sudden changes, e.g., caused by large-scale disasters, requires careful management of limited and varying resources. The rapidly growing big data from both physical sensors and social media in real-time suggest an unprecedented opportunity for information technology to enable increasing efficiency and effectiveness of adaptive resource management techniques in response to sharp changes in supply and/or demand on critical infrastructures. Within the general areas of resilient infrastructures and big data, this project will focus on the integration of heterogeneous Big Data and real-time analytics that will improve the adaptive management of resources when critical infrastructures are under stress. The integration of heterogeneous data sources is essential because many kinds of physical sensors and social media provide useful information on various critical infrastructures, particularly when they are under stress. This Research Coordination Network (RCN) will promote meetings and activities that stimulate and enable new research on integration of heterogeneous physical sensor data and social media for real-time big data analytics in support of resilient critical infrastructures such as transportation and healthcare in smart cities. As first example, the RCN will support participation from young faculty attending the Early Career Investigators' Workshop on Cyber-Physical Systems in Smart Cities (ECI-CPS) at CPSweek (April of each year) and young faculty attending the Workshop on Big Data Analytics for Cyber-physical Systems (BDACPS). As a second example, the RCN will support contributions to a Special Track on Big Data Analytics for Resilient Infrastructures at the IEEE Big Data Congress. As a third example, the RCN will support participation in International meetings organized by other countries, e.g., Japan's Big Data program by Japan Science and Technology Agency (JST). The project will also maintain a repository of research resources. Concretely, the RCN will actively collect and make readily available public data sets (e.g., physical and social sensor data) and software tools (e.g., to support real-time big data analytics). The technologies and tools that arise from RCN-enabled research will be applied to socially and economically impactful areas such as reducing congestion and personalized healthcare in smart cities.

城市通过共享的民用基础设施（如交通和医疗保健）提供方便和有效的设施和便利设施。使这些关键基础设施能够抵御突然的变化，由于大规模灾害造成的自然灾害，需要对有限和变化的资源进行认真管理。来自物理传感器和社交媒体的实时大数据的快速增长为信息技术提供了前所未有的机会，可以提高自适应资源管理技术的效率和有效性，以应对关键基础设施供应和/或需求的急剧变化。在弹性基础设施和大数据的一般领域内，该项目将重点关注异构大数据和实时分析的集成，这将在关键基础设施受到压力时改善资源的自适应管理。异构数据源的集成是必不可少的，因为许多类型的物理传感器和社交媒体提供了有关各种关键基础设施的有用信息，特别是当它们处于压力之下时。该研究协调网络（RCN）将促进会议和活动，促进和实现对异构物理传感器数据和社交媒体的集成进行实时大数据分析的新研究，以支持智能城市中的交通和医疗保健等弹性关键基础设施。 作为第一个例子，RCN将支持参加CPSweek（每年4月）智能城市网络物理系统（ECI-CPS）早期职业调查员研讨会的年轻教师和参加网络物理系统大数据分析研讨会（BDACPS）的年轻教师的参与。作为第二个例子，RCN将在IEEE大数据大会上支持对弹性网络结构的大数据分析特别跟踪的贡献。第三个例子是，区域网络将支持参加其他国家组织的国际会议，例如，日本科学技术厅（JST）的大数据计划。该项目还将维持一个研究资源库。具体而言，区域网络将积极收集和提供公共数据集（例如，物理和社会传感器数据）和软件工具（例如，支持实时大数据分析）。RCN研究产生的技术和工具将应用于对社会和经济有影响的领域，例如减少智能城市中的拥堵和个性化医疗保健。