CPS: Synergy: Collaborative Research: Extracting Time-Critical Situational Awareness from Resource Constrained Networks

该项目的目标是促进作战中心在资源受限的不确定环境中，例如在灾难恢复或搜救任务中遇到的环境中，及时从现场部署的节点检索动态态势感知信息。这是一个重要的网络物理系统问题，从系统和平台两个层面以及系统层面展开。技术进步允许部署能够返回丰富内容(例如，视频/图像)的现场节点，这些内容可以显著帮助救援和恢复。然而，在资源有限的情况下获取、处理和提取与业务有关的内容的技术的开发带来了重大的跨学科挑战，本项目将解决这一挑战。该项目的重点将放在这些任务背后的基础科学上，通过内部实验进行验证，并根据领域专家的输入进行现场测试。为了实现该项目的愿景，将开发一套算法和协议，以：(A)智能地激活现场传感器并获取和处理数据以提取语义相关信息；(B)制定可表达和有效的查询，使相关态势感知信息能够在遵守资源限制的情况下近实时检索；以及(C)实施促进经济高效的查询传播和响应检索的网络结构。这项研究汇集了计算科学中的多个子学科，包括计算机视觉、数据挖掘、数据库和网络，并利用受威胁的计算/通信资源理解信息管理背后的科学原理。该项目将在灾难响应等应用程序中提供有效的态势感知方面产生重大而广泛的影响。最近的《世界灾难报告》指出，在过去十年中，有100多万人死于灾难，造成1.5万亿美元的损失；这项研究有可能大幅减少这些数字。其他可能的应用是执法和环境监测。该项目将促进一个强大的跨学科教育项目，为本科生和研究生提供实验和原型开发的经验。该项目将大力强调更广泛的社区参与，并与针对未被充分代表的学生和少数族裔的项目合作。

he goal of this project is to facilitate timely retrieval of dynamic situational awareness information from field-deployed nodes by an operational center in resource-constrained uncertain environments, such as those encountered in disaster recovery or search and rescue missions. This is an important cyber physical system problem with perspectives drawn at a system and platform level, as well as at the system of systems level. Technology advances allow the deployment of field nodes capable of returning rich content (e.g., video/images) that can significantly aid rescue and recovery. However, development of techniques for acquisition, processing and extraction of the content that is relevant to the operation under resource constraints poses significant interdisciplinary challenges, which this project will address. The focus of the project will be on the fundamental science behind these tasks, facilitated by validation via both in house experimentation, and field tests orchestrated based on input from domain experts.In order to realize the vision of this project, a set of algorithms and protocols will be developed to: (a) intelligently activate field sensors and acquire and process the data to extract semantically relevant information; (b) formulate expressive and effective queries that enable the near-real-time retrieval of relevant situational awareness information while adhering to resource constraints; and, (c) impose a network structure that facilitates cost-effective query propagation and response retrieval. The research brings together multiple sub-disciplines in computing sciences including computer vision, data mining, databases and networking, and understanding the scientific principles behind information management with compromised computation/communication resources. The project will have a significant broader impact in the delivery of effective situational awareness in applications like disaster response. The recent :World Disaster Report" states that there were more than 1 million deaths and $1.5 trillion in damage from disasters within the past decade; the research has the potential to drastically reduce these numbers. Other possible applications are law enforcement and environmental monitoring. The project will facilitate a strong inter-disciplinary education program and provide both undergraduate and graduate students experience with experimentation and prototype development. There will be a strong emphasis on engaging the broader community and partnering with programs that target under-represented students and minorities.