Situational awareness—the need to harvest information surrounding an event and rapidly process and adapt to it—is critical in emergency management and response situations. Situational awareness applications integrate data from diverse sources, including physical sensor networks, and communicate analyzed data to users, such as responders in the field. Field responders can use this information to improve their effectiveness and to ensure their safety and that of others.
Situational awareness applications require rapid deployment and must operate in real-time. In many cases, they require reprogramming to accommodate unanticipated situations. The systems need to be robust when hardware or software fails, including failures of computer nodes and communication channels. Such cases may require dynamic reconfiguration to facilitate operation in a degraded mode.
RENCI is a collaborator in the R3SAR (Rapidly deployable, Robust, Real-time Situational Awareness and Response) research project, funded by a National Science Foundation grant. The project investigates high-level, high-productivity programming models for implementing situational awareness application systems, spanning devices with varying computational and communication capabilities—from mobile phones used by emergency responders on the ground to environmental sensors, satellites, and supercomputers. The approach is based on a modular cross-layered architecture that combines a data-centric descriptive programming model with an overlay-based communication model to support implementation of efficient and robust applications. The underlying layers of the architecture will provide mechanisms and basic policies for adaptation, while the programming model will facilitate fast and effective human intervention at a higher level.
- Rob Fowler (Principal Investigator)
- Erik Scott
- Jeff Heard