Environmental Monitoring with Hybrid Sensor Networks

Nirupama BulusuComputer Science Department

Portland State Universityhttp://www.cs.pdx.edu/~nbulusu

US-France Young Engineering Scientists Symposium – October 2007Copyright © Nirupama Bulusu

Coastal Monitoring

(Source: http://www.ccalmr.ogi.edu/CORIE/)

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 RiverSource: http://www.stccmop.org

Online adaptive sampling algorithmsguide 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: http://www.stopthetoad.org.au

Concurrent Random Sensing

SOURCE

CONCURRENT RANDOM SAMPLING SENSORS

Each sensor concurrently and randomly samples a source at a rate much lower than the traditional sensing rate

Sense then compressCompress while sensing

Recover exactly in ideal case (no noise)Recover with bounded error (noise)

Research InterestsTheoryData 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 ImplementationCascades: Python-based framework for hybrid sensor networks (NOSSDAV05, MMCN06)SenseTK: Application-specific video sensing toolkit (MMCN07)

ApplicationsCoastal Monitoring (DCOSS07)

Cane-Toad Monitoring(IPSN/SPOTS 05)

Tip of the Iceberg?