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  • Sensor-based detection technologies for identifying events: abnormal ground motion, extreme weather, flooding, fires, human illness epidemics and general wellness;

  • Fault-tolerant networks that connect sensor systems together.

  • New methods for integrating sensor data and sensing analysis in the cloud.

  • New algorithms for data fusion and machine learning for detecting anomalous events, and research that characterizes tradeoffs among rates of false positive alarms, false negative alarms, and time to detection;

  • Systems for delivering actionable alerts/information to responders and communities using multiple networks.

  • Evaluation of the above research in campus testbeds at UCI/Caltech, culminating in a pilot study/drill at one of the Indian institutions with regional experience in disaster management.


This work is funded under NSF grant #CNS-1143666.