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（例如交通和医疗保健）提供现成且高效的设施和便利设施。要使此类关键基础设施能够应对突然变化（例如由大规模灾难引起的变化），需要仔细管理有限且变化的资源。来自物理传感器和社交媒体的实时快速增长的大数据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面临压力时改善资源的自适应管理。异构数据源的集成至关重要，因为多种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，特别是当它们面临压力时。该研究协调网络（RCN）将促进会议和活动，以刺激和推动异构物理传感器数据和社交媒体集成的新研究，以进行实时大数据分析，以支持智慧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 将支持年轻教师参加 CPSweek（每年 4 月）举办的智能城市网络物理系统早期职业研究者研讨会 (ECI-CPS)，以及参加大数据分析研讨会的年轻教师用于网络物理系统（BDACPS）。作为第二个例子，RCN 将支持在 IEEE 大数据大会上为弹性基础设施大数据分析特别专题做出贡献。第三个例子，RCN 将支持参加其他国家组织的国际会议，例如日本科学技术振兴机构 (JST) 的日本大数据计划。该项目还将维护一个研究资源库。具体而言，RCN 将积极收集并提供易于使用的公共数据集（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 支持的研究产生的技术和工具将应用于具有社会和经济影响的领域，例如减少智能城市的拥堵和个性化医疗保健。