The 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.