Center for Embedded Networked Sensing CENS

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Center for Embedded Networked Sensing(CENS)

• Deborah Estrin
• Center for Embedded Networked Sensing (CENS),
  Director
• UCLA Computer Science Department, Professor
• Work summarized here is largely that of students
  and staff at CENS

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Embedded Networked Sensing

• Micro-sensors, on-board processing, wireless
  interfaces feasible at very small scale--can
  monitor phenomena up close
• Enables spatially and temporally dense
  environmental monitoring
• Embedded Networked Sensing will reveal
  previously unobservable phenomena

Contaminant Transport
Ecosystems, Biocomplexity
Marine Microorganisms
Seismic Structure Response
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ENS Architecture Drivers
DRIVERS
TECHNICAL CAPABILITIES
Adaptive Self-Configuring Wireless Systems
Varied and variableenvironments
Energy and scalability
Distributed Signal and Information Processing
Heterogeneity of devices
Networked Info-Mechanical Systems
Smaller component size and cost
Embeddable Microsensors
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CENS Systems under design/construction

• Ecosystem processes
• Microclimate monitoring
• Triggered image capture
• Canopy-net (Wind River Canopy Crane Site)
• Contaminant Transport
• County of Los Angeles Sanitation Districts
  (CLASD) wastewater recycling project, Palmdale,
  CA
• Seismic monitoring
• 50 node ad hoc, wireless, multi-hop seismic
  network
• Structure response in USGS-instrumented Factor
  Building w/ augmented wireless sensors

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Ecosystem Monitoring

• Sensor system logical components
• Tasking, configuration (sample rates, event
  definition, triggering)
• Data Transport
• Device management, sample manipulation and
  caching with timing
• Duty cycling
• Platforms Tiered architecture
• Mica2 motes (Atmega 128L, 433 MHz Chipcon radio)
  w/TOS with Sensor Interface Board hosting in
  situ sensors
• Microservers (xscale/strongarm) are solar
  powered, run linux, strongarm/xscale based
• Pub/sub bus over 802.11 to databases, vis, anal
  tools, Internet
• Other important examples of habitat monitoring
  systems
• Berkeley/Intel GDI and Botanical gardens

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Networked Info Mechanical Systems (NIMS)

• Robotic, aerial access to full 3-D environment
• Sensing Diversity
• Diverse sensing types (high end)
• Diverse locations, perspectives, topologies
• Enable sample acquisition
• Coordinated Mobility
• Adapt resource placement to minimize sensing
  uncertainty
• Calibration, resource delivery, data mule
  services
• NIMS Infrastructure
• Enables speed, efficiency
• Low-uncertainty mobility
• Provides resource transport for sustainable
  presence

( Kaiser, Pottie, Estrin, Srivastava, Sukhatme,
Villasenor)
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Contaminant Transport Monitoring Palmdale Pivot
Study

• Regulators require proof that the nitrate-laden
  treated water will not impact groundwater if used
  for irrigation.
• monitoring wells cost of 75K each
• Vertical array of sensors will measure rate of
  diffusion of water and nitrate levels
• Observed nitrate levels, local model will
  trigger contribute to field-wide estimate of
  hazardous Nitrate levels
• Field wide estimate re. concentrations and trends
  fed back to sprinkler quantity

T. Harmon
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Broadband ad hoc seismic array
P. Davis

• Core requirement is multi-hop time
  synchronization to eliminate dependence on GPS
  access at every node

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Research Challenge Distributed Representation,
Storage, Processing

• In network interpretation of spatially
  distributed data
• Statistical or model based filtering
• In network event detection and reporting
• Direct queries towards nodes with relevant data
• Trigger autonomous behavior based on events
• Expensive operations high end sensors or
  sampling
• Robotic sensing, sampling
• Support for Pattern-Triggered Data Collection
• Multi-resolution data storage and retrieval
• Index data for easy temporal and spatial
  searching
• Spatial and temporal pattern matching
• Trigger in terms of global statistics (e.g.,
  distribution)
• Exploit tiered architectures

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Research ChallengeCalibration, or lack thereof
Un-calibrated Sensors

• Storage, forwarding, aggregation, triggering
  useless unless data values calibrated
• Calibration correcting systematic errors
• Sources of error noise, systematic
• Causes manufacturing, environment, age, crud
• Traditional in-factory calibration not sufficient
• must account for coupling of sensors to
  environment
• Nearer term is to identify faulty sensors and
  flag data, discard for in network processing
• Significant concern that faulty sensors can wreak
  havoc on in network processing

72º
Factory Calibrated Sensors T0
72º
72º
72º
72º
72º
72º
Factory Calibrated Sensors Later
62º
70º
72º
71º
72º
72º
Dust
Bychkovskiy , Megerian, Potkonjak
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Research ChallengeMacroprogramming

• How to specify what, where and when?
• data modality and representation,
  spatial/temporal resolution, frequency, and
  extent
• How to describe desired processing?
• Aggregation, Interpolation, Model parameters
• Triggering across modalities and nodes
• Primitives
• Annotated topology/resource discovery
• Region identification and characterization
• Intra-region coordination/synch
• System health data, alerts
• Topology, Resources (energy, link, storage)
• Sensor data management (buffering, timing)

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What will it take to be real/deployable?

• Users need
• Survivability (configuration, tasking)
• Precision (calibration, time, location)
• Flexibility (taskable, programmable)
• Heterogeneity (sensor modalities, tiered
  architecture)
• For Starters
• System health monitoring (faulty sensors,
  connectivity)
• Duty cycle management (flexible/adaptive to
  bursty events)
• Multi-hop over air programming/VM support
• Closing the design, evaluation cycle
• Rich simulation, emulation, visualization,
  prototyping environment
• Authoring systems
• Deployed-And-Used System experience !
• SW and hardware re-use/sharing