Environmental Monitoring with Hybrid Sensor Networks Nirupama Bulusu Computer Science Department Portland State University

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Presentation transcript:

Environmental Monitoring with Hybrid Sensor Networks Nirupama Bulusu Computer Science Department Portland State University US-France Young Engineering Scientists Symposium – October 2007 Copyright © Nirupama Bulusu

Coastal Monitoring (Source: Multi-scale data assimilation combines observational data with numerical data models to produce an estimated system state for the physical process. CORIE: A pilot EOFS for the Columbia River Source: Online adaptive sampling algorithms guide mobile cruise vehicles to reduce uncertainty in the data assimilation

Cane-Toad Monitoring Acoustic vocalizations can be used to distinguish and census different amphibians – call rate, call duration, amplitude-time envelope, waveform periodicity, pulse-repetition rate, frequency modulation, frequency and spectral patterns Frog 1 Frog 2 Frog 3 (Cane toad) Source:

Concurrent Random Sensing Each sensor concurrently and randomly samples a source at a rate much lower than the traditional sensing rate Sense then compress Compress while sensing Recover exactly in ideal case (no noise) Recover with bounded error (noise)

Research Interests Theory Data Compression (EWSN07) and Compressive Sensing Data Modeling (Emnets05, IWASN06) Data Assimilation Localization (IEEE PC00, ACM TECS04, Sensys04, IEEE TMC05) Camera Calibration Network optimization (BaseNets05, DCOSS07) Systems Implementation Cascades: Python-based framework for hybrid sensor networks (NOSSDAV05, MMCN06) SenseTK: Application-specific video sensing toolkit (MMCN07) Applications Coastal Monitoring (DCOSS07) Cane-Toad Monitoring (IPSN/SPOTS 05)

Tip of the Iceberg?