Deborah Estrin

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Wireless Sensing Systems from Eco-systems to
Human-systems

• Deborah Estrin
• destrin_at_cs.ucla.edu
• Work summarized here is that of students, staff,
  and faculty at CENS
• We gratefully acknowledge the support of our
  sponsors, including the National Science
  Foundation, Nokia, Intel Corporation, Cisco
  Systems Inc., Crossbow Inc., Agilent, Microsoft
  Research, Sun Inc., and the participating
  campuses.
• http//research.cens.ucla.edu

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Embedded Networked Sensing Motivation

• Many critical issues facing science, government,
  and the public call for high fidelity and real
  time observations of the physical world
• Networks of smart, wireless sensors can reveal
  the previously unobservable
• Designing physically-coupled, robust, scalable,
  distributed-systems is challenging
• The technology will also transform the business
  enterprise (from inventory to manufacturing), and
  human interactions (from medical to social)

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Embedded Networked Sensing
Embed numerous devices to monitor the physical
world Network to monitor, coordinate and perform
higher-level identification Sense and actuate
adaptively to maximize information return
In-network and multi-scale processing algorithms
to achieve Scalability for densely deployed
sensors Low-latency for interactivity,
triggering, adaptation Integrity for challenging
system deployments
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A Walk Through History
1996
1997
1998
1999
2000
2001
2002
2003
2004
2005
2006
Srivastava, et al
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Future Expanding Sensor Suite
present
future
Physical Sensors Microclimate above and below
ground
abiotic
Chemical Sensors gross concentrations
Chemical Sensors trace concentrations
Acoustic, Image sensors with on board analysis
Acoustic and Image data samples
DNA analysis onboard embedded device
biotic
Sensor triggered sample collection
Organism tagging, tracking

• Commercially available devices available for many
  physical and chemical measures
• Advancements in sensor technologies will further
  transform NEON as new capabilities broaden
  physical, chemical, and biological in situ,
  autonomous, observations

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Resource and Energy Constraints as Drivers

• Dominance of communication over storage and
  processing
• Dominance of Rx over Tx
• The power vs. energy efficiency, performance
  choice
• Achieving sustained operation
• High cost of sensor sampling

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M. Srivastava
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Communication vs. Storage vs. Processing
Energy/bit sent gtgt Energy/bit stored gt Energy/op

• Architecture implications in-network processing
  storage (Diffusion, TinyDB etc.)

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M. Srivastava
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Low Power vs. Energy Efficiency, Performance

• Architecture implications role-adaptive
  architectures
• Nodes with with antipodal resources, e.g. PASTA,
  LEAP

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M. Srivastava
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Sustaining Long-term Deployments

• The chimera of longevity
• Batteries require replacement!
• Current state
• about one year using mote class devices with
  simple sensors periodically sampling at low rates
  and duty cycles (lt 1)
• about a week using microserver class devices with
  sophisticated high rate sensing modalities
• Harvesting-aware nodes promise 20 years at
  20-60 duty cycle

• Architecture implications energy neutral
  operation
• HelioMote, Trio/Prometheus, DuraNode
• Harvesting-aware duty cycling, routing.

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M. Srivastava
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Technology challenges
Objectives
Constraints

• Embeddable, low-cost sensor devices
• Robust, portable, interactive systems
• Data integrity, system dependability
• Programmable, transparent systems
• Multiscale sensing and actuation

• Sensing channel uncertainties
• Environmentally compatible deployment
• Limited resources node, infrastructure
• Complexity of distributed systems
• No ground truth

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Status Many first generation hw/sw system
components exist
Localization Time Synchronization
System Mgmt
In Network Processing
Multiscale
Power on demand
Event Detection
Routing and Transport

• Reusable, Modular, Flexible, Well-characterized
  Services/Tools
• Routing, Reliable transport, Plug and Play
• Time synchronization, Energy Harvesting, Power on
  demand, Localization, Self-Test
• In Network Processing Tasking, Filtering,
  Triggering, Fault detection, Multiscale
  coordinated / actuation
• Simulation, Testbeds, Programming Abstractions,
  Application authoring tools, embedded statistical
  tools

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Technology History and Themes Environmental
Monitoring Observatories Field Inspired Research
Themes Participatory Sensing
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Environmental monitoring applications spatial
variations and heterogeneity
Precision Agriculture, Water quality management
Impact of fragmentation on species diversity
Earth structure inhomogeneities
Algal growth as part of eutrophication
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ENS Observatories
Terrestrial
Seismic
createprogrammable, distributed,
multi-modal, multi-scale, multi-use
observatories to address compelling science and
engineering issues and reveal the previously
unobservable. From the natural to the built
environment From ecosystems to human systems
Contaminant transport
Aquatic
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Visualization and Navigation through ENS Space

• Example Keyhole/Google Earth as one approach
  towards navigation, visualization, data sharing,
  and attracting a community of users via the Web.

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NIMS RD Merced and San Joaquin River Confluence
confluence
Sonar-based bathymetry (depth)
(2-day survey Harmon, Kaiser, et al)
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Data from Mexico Seismic Array Pakistan
Earthquake
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Science applications are historical drivers for
information technology development and deployment

• Early embedded sensing applications
• Biological and Earth Sciences
• Environmental, Civil, Bio Engineering
• Public health, Medical research
• Agriculture, Resource management

• Science is early adopter because the technology
  is transformative and research tolerates risk
• The same technology will transform the business
  enterprise
• Important historical precedents
• Weather modeling--early computing
• Scientific collaboration--Internet
• Experimental physics (CERN)--WWW
• Computational science--Grid computing

• Embeddable device developments
• Energy-conserving platforms, radios
• Miniaturized, autonomous, sensors
• Standardized software interfaces
• Self-configuration algorithms

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Technology History and Themes Environmental
Monitoring Observatories Field Inspired Research
Themes Participatory Sensing
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Lessons from the field...
Early themes Thousands of small devices Minimize
individual node resource needs Exploit large
numbers Fully autonomous systems In-network and
collaborative processing for longevity
optimize communication

• New themes
• Heterogeneity
• Tiered systems architecture to optimize system as
  a whole
• Inevitable under-sampling (in time or space) with
  homogeneous sensing
• Exploit multiple modalities, multiple scales, and
  mobility
• Interactivity
• Coupled human-observational systems online
  tasking, analysis, visualization
• In-network and collaborative processing for
  responsiveness, data quality, rapid and
  iterative deployment
• Monitoring the monitors calibration, self
  test, validation

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Mobility/Actuation is an important dimension of
heterogeneity

• While ENS is a revolutionary technology for dense
  sensing
• the likelihood of under-sampling critical
  phenomena is surprisingly high
• meeting sampling objectives is sometimes
  impractical with static nodes
• Mobility is a critical amplifier of system
  coverage, from highly constrained articulation,
  to longer range spatial traversals.
• Articulation magnifies effective sensor range and
  spatial diversity
• Infrastructure-supported mobility (NIMS) enables
  sensor diversity
• Enables adaptive, fidelity-driven, 3-D sampling

Networked Info Mechanical Systems (NIMS)
Kaiser, Pottie, et al
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Robotic systems provide sensor and spatial
diversity
Deployments James Reserve Phenology, Wind River
Canopy Crane, Public Health Media Creek
Future Development 3-Dimensional, Portability
and rapid deployment
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Another essential element in heterogeneous
system the user

• Whereas we focused initially on very long lived,
  autonomous systems design, interactive and rapid
  deployments are high value.
• Interactive systems take advantage of human
  observer, actuator
• Addresses critical issues such as adaptive
  sampling, topology adjustment and faulty sensor
  detection
• Requires real time data access, model based
  analysis, and visualization in the field

Daily Average Temperature (Geostatistical
Analyst) Aspect (Spatial Analyst) Slope (Spatial
Analyst) Elevation (Calculated from Contour
Map) Aerial Photograph (10.16cm/pixels)
Coupled Human-Observational Systems transform
physical observations from batch to interactive
process
Hansen, Hamilton, Graham, et al
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Rapid Deployments powerful usage model

• RD Systems deployed many times for short
  durations
• Powerful usage model for environmental assessment
• trade temporal for spatial density and coverage
• Short deployment duration enables
• Frequent calibration and maintenance
• User presence with increased functionality, data
  quality
• Research challenges
• Rapid setup, data return
• System visibility mechanisms tools
• Models to inform deployment adjustments as
  deployment unfolds
• In-field calibration, data integrity tools

Ramanathan, Kohler, Hansen, et al
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Increasing role of statistical models and methods

• Experimental design and sensor layout adaptive,
  iterative schemes for deployment
• Botanical gardens microclimate system design
  Source of variability via PCA, Optimization via
  ILP
• Palmdale soil observation network design
  Geospatial statistical methods for optimal sensor
  placement

• Data integrity robust procedures for aggregation
  and analysis
• Fluorometer measurements at lake Fulmor Running
  medians

• Spatio-temporal models flexible or
  nonparametric descriptions of signals
• Media creek nitrate studies spline based
  estimators

• Opportunistic measures identifying and
  integrating existing sources of data from other
  engineered systems
• Elevator tracking for structural health
  monitoring wavelet coherence

Hansen, et al
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Imagers as biological sensors
Cyclops

• A vision sensor that mates with Mote-class
  devices enables
• Large Numbers
• Information about the statistics of the
  experiment
• Minimum Infrastructure
• Diversity of pose, distance, angle
• Applications
• Occluded environments
• Local observations in Large space

Agilent Technology
Rahimi, Srivastava, et al
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In situ imaging applications
Ecology and Agriculture
Spectroscopic, size, shape analysis

• Plant species studies phenology, fruiting
  conditions, trends, timing
• Animal species studies birds and reptiles
• Pitfall Traps measure population of reptiles,
  timely animal identification and notification for
  tag and release.
• Bird nestbox measure distribution of occupancy,
  occupancy vs. time of day and condition of the
  nest, number of eggs/young
• LED as flash for night images Infrared for birds

June 2006
August 2006
Ahmadian, Rahimi, Graham, et al
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Leverage context to apply on-board processing to
the application
The blue lines are the output of automatic image
processing algorithms applied to cyclops images
over 5 minute intervals.
Ahmadian, Burke, Rahimi, Laufer
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Environmental Monitoring Observatories Technology
History and Themes Field Inspired Research
Themes Participatory Sensing From Ecosystems to
Human Systems
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Deployment modes offer different information
return and tradeoffs

• Other key tradeoffs
• Temporal vs Spatial density
• Temporal density vs. extent
• Sensible features vs. density

Automated Mobility
Spatial Density
Static
Handheld Mobility
Remote Sensing
Spatial Extent
Ramanathan, Goldman, et al
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Participatory Sensing

• ENS is revealing the previously unobservable in
  science applications
• Multi-scale data and models to achieve context,
  and in network processing and mobility to achieve
  scalability (communication, energy, latency)
• Automatically geocoded and uploaded participatory
  sensing data promises to make visible human
  concerns that were previously unobservableor
  unacceptable
• Urban sensing applications will leverage the
  millions of cell phone acoustic, image and
  bluetooth-connected sensors
• Internet search, blog, and personal feeds, along
  with automated location tags, to achieve context,
  and in network processing for privacy and
  personal control

?
?
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Range of Application Types

• Directed Sensing Applications
• Eco-PDA
• Self-administered health diagnostics
• Public/community health

• Citizen Sensing
• Participatory urban planning
• Place-aware social networking
• Distributed journalism

• Enabling Elements
• Over 2 x 109 users worldwide of cell phones.
• Automated geo-coding and pervasive connectivity
• Image and acoustic as data and metadata
• Local processing for data quality and triggering
• Spatial interface to data and authoring

Burke, Hansen, Srivastava, Parker, Redi, et al
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Participatory Sensing Potential

• Participatory Sensing
• Enable massive distributed, parallel collection
  of media
• Contextualize data to data for automated
  classification, verification.
• Leverage Partisan core to increase credibility
  and privacy for participants.
• Inspire innovative algorithms for managing the
  sampling process, opt-in location info, analysis
  tools, middleware, etc.

• Public health impact
• Personal/home indicators/testing
• Human activity patterns
• Municipal public health factors
• e.g., Air quality relationship to chronic health
  issues (asthma
• retrospective analyses of chronic health problem
  causes.

• Natural resource mgmt
• Facilitate high-quality field data entry
• Leverage signal processing for validation at time
  of entry
• Analyzable image-based data entry
• Adaptive protocols that depend on data collected
  and other (multi-scale) environmental conditions

Burke, Hansen, Srivastava, Parker, Redi, et al
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Common Application Style Observation Campaigns

• Real urban examples of citizen concerns (web
  based)
• Bicycling to work lack of adequate facilities
• Cell phone use in cars
• Does red light photo program work
• Fallen (public) fruit (fallenfruit.org)
• Impact of lack of sidewalks
• Items sold to children that resemble real bad
  objects
• Lawn estimated time-to-death without water
• Mobile phone Amber Alert (codeamber.org)
• Neighborhood maintenance, visible decay

Partisan targets Noise levels in different types
of locations Traffic at intersections (light
timing, stop signs) Flooded storm drains
Violations of carpool lanes Park or street
maintenance issues (uneven sidewalks) Public
transportation stop occupancy in LA Power outage
documentation scope time (05-1914)Speed
humps slowing traffic in neighborhoods
(04-1281-S2) Timelapse collage of a
location Water quality measurements (photograph
simple indicators)
Numbers in parentheses are LA City Council file
numbers.
Burke, Hansen, Srivastava, Parker, Redi, et al
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Concerns about participation Privacy

• Need for personal configuration and control of
  shared data
• Close to the sensor source not on the backend
• Lessons from microdata release Resolution
  control, blurring, subsampling, local buffering
  and filtering
• Guidelines for Privacy and Selective Sharing
• Context of data should be verifiable to a
  resolution with which provider is comfortable
  and as needed by application
• Policies for selective sharing should be
  implemented as an automated component of a
  sensing system.
• Decisions about data sharing depend often on
  location and time.
• HCI for configurability of privacy/security
  policies is critical (Bellovin)
• Data Integrity also matters
• Verify geocoding
• Corroborate sensor data

Burke, Hansen, Srivastava, Parker, Redi, et al
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Engineering, enterprise, civic, and consumer
applications will eventually dominate

• As the technology matures we expect to find
  wide-reaching applications in the built
  environment, health care, and throughout the
  business enterprise.
• Todays systems focus on early-adopter
  science users (reveal the previously
  unobservable)

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Conclusions

• New themes will drive next 5-10 years of wireless
  sensing systems
• Mobile, Multi-scale, Multi-modal
• Integrity, sensing, participatory and interactive
  
• Participatory sensing public health, social,
  personal
• Publishing and sharing sensor data Slogging
  (MH)
• New advanced integrated-sensor development will
  take time and investment
• Technology development model
• Early-to-application to leverage deployments
  and resulting data provides feedback to system
  innovation, from theory to algorithm

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Acknowledgments

• CENS colleagues
• Jeff Burk, Jeff Goldman, Eric Graham, Mark
  Hansen, Tom Harmon, Jenny Jay, Bill Kaiser, Eddie
  Kohler, Greg Pottie, Mohammad Rahimi, Phil
  Rundel, Mani Srivastava, Gaurav Sukhatme, John
  Villasenor and many others...
• Students (current and recently current)
• Lewis Girod, Ben Greenstein, Martin Lukac, Andrew
  Parker, Nithya Ramanathan, Sasank Reddy, Thomas
  Schmid, Tom Schoellhammer, Thanos Stathopoulos,
  and many others...
• Funding agencies and Industrial Supporters
• NSF
• Intel, Nokia, MSR, Cisco