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

该项目的目标是在资源受限的不确定环境中，例如在灾难恢复或搜索和救援任务中遇到的环境中，促进操作中心从现场部署的节点及时检索动态态势感知信息。这是一个重要的网络物理系统的问题，在系统和平台级别，以及在系统的系统级别绘制的观点。技术进步允许部署能够返回丰富内容的现场节点（例如，视频/图像），可以显著帮助救援和恢复。然而，在资源有限的情况下，开发获取、处理和提取与行动有关的内容的技术构成了重大的跨学科挑战，本项目将解决这些挑战。该项目的重点将是这些任务背后的基础科学，通过内部实验和基于领域专家输入的现场测试进行验证。为了实现该项目的愿景，将开发一套算法和协议：（a）智能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）制定表达和有效的查询，使相关的态势感知信息的近实时检索，同时遵守资源的限制;和（c）强加一个网络结构，促进成本效益的查询传播和响应检索。该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，并了解信息管理背后的科学原理与妥协的计算/通信资源。该项目将对在灾害应对等应用中提供有效的态势感知产生更广泛的影响。最近的《世界灾害报告》指出，在过去十年中，灾害造成100多万人死亡，损失1.5万亿美元;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。该项目将促进强大的跨学科教育计划，并为本科生和研究生提供实验和原型开发经验。将有一个强烈的重点是参与更广泛的社区和与针对代表性不足的学生和少数民族的计划合作。

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.