Extracting Time-Critical Situational Awareness from Resource Constrained Networks

Abstract:

Overall Objective. The goal of this project is to facilitate the timely retrieval of dynamic situational awareness information from field deployed nodes by an operational center in disaster recovery or search and rescue missions, which are typically characterized by resource-constrained uncertain environments. 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. The research will hinge on sound scientific reasoning facilitated by strong experimentation on testbeds available at UCR and UCI.

Intellectual Merit. Towards realizing a networked system that facilitates the retrieval of time-critical, operation-relevant situational awareness, this project will focus on the following research tasks.

Task A: Resource-Constrained Data Acquisition and Analysis. This task looks at how to reconfigure the network and adapt video analysis in real time to meet different (sometimes conflicting) application requirements, given resource constraints.

Task B: Information Fusion Under Resource Constraints. This task proposes methods to locally process and fuse the content generated, given the query needs and resource constraints. It also considers how to summarize the content received in response to the queries to facilitate further analysis at the operation center.

Task C: Cost-effective Query Formulation and Retrieval. This task will address challenges in query formulation, refinement and retrieval, including (i) prioritizing queries as per importance criteria, (ii) effective query dissemination in the field, and (iii) effective retrieval of the sensed information. The project will bring together multiple sub-disciplines in the computing sciences including computer vision, data mining, databases, and networking. It will lead to understanding the fundamental scientific principles behind information management with compromised computation/communication resources.

Broader Impacts. Efficient situational awareness information retrieval under severe resource limitations is critical in applications like disaster response. The recent World Disaster Report states that there were more than 1 million deaths and over $1.5 trillion damages from disasters within the last decade. The CPS Vision Statement identifies the need for technologies that can "dramatically increase the situational awareness of emergency responders and enable optimized response through all phases of disaster events." Other possible applications are law enforcement or environmental monitoring. The PIs will build a cross-disciplinary education plan at both UCR and UCI that includes new course development and new laboratories. They will also engage the broader community and partner with programs that target under-represented students. The project expands on existing testbed resources at UCR and UCI that can be used for education and research based on this project and its offshoots.