Instrumenting the Physical World With Wireless Sensor Networks

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Instrumenting the Physical World With Wireless
Sensor Networks

• Deborah Estrin, Lewis Girod
• UCLA Computer Science Department
• destrin,girod_at_cs.ucla.edu

Greg Pottie, Mani Srivastava UCLA Electrical
Engineering Department mbs,pottie_at_ee.ucla.edu
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Wireless, Distributed Sensing

• Why Distributed Sensing?
• Closer to phenomena
• Improved opportunity for LOS
• 1/r4
• Why Wireless?
• Ad hoc deployment
• Remote locations
• Why Distributed Processing?
• Energy budget for comms
• Moores law brings down cost of local processing,
  does not affect radio propagation

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Motivating Application

• Habitat Monitoring
• Support data collection, model devel
• Detailed data from small samples
• Models used to define local algorithms
• Soil, air, water chemistry
• Species migration, behavior
• Imaging and acoustics to localize, identify and
  track animals
• Tagging
• Remote locations, no infrastructure
• Distributed processing critical
• e.g. acoustic triggered imager

Species Detection and Tracking
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Key Techniques

• Collaborative signal processing
• Coherent processing on local clusters
• Non-coherent processing across clusters
• Exploit redundancy
• To achieve good coverage with ad-hoc deployment
• To overcome environmental limitations
  (obstructions)
• Extend system lifetime

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Key Techniques

• Adaptive Fidelity
• Tradeoffs energy cost, accuracy, latency
• Adapt based on detection of events, e.g.
• turn on more nodes,
• increase sampling rate,
• increase duty cycle
• Tiered architectures
• Improve system lifetime and capability
• Long/short range radios
• High-capacity system can reduce load on low power
  neighbors

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Problem Areas Approach

• Vertical integration, very little reuse of
  mechanisms
• First get experience build and characterize
• Then try to factor out reusable modules and
  principles
• Many problem areas
• Time synchronization
• Localization (local global coordinate systems)
• Tiered architectures, leverage of high energy
  nodes
• Geographical energy aware routing (burnout
  avoidance)
• Density adaptive algorithms

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Localization

• Physical node location is a critical parameter
• Tasks and results are expressed in terms of
  location
• Enables geographical routing
• Energy cost is a function of sum of physical hop
  lengths, processing cost, and channel efficiency
  (e.g. overhead related to density)
• Granularity requirements are app-dependent
• Coherent signal processing such as beamforming
  requires precise relative location and
  orientation
• Routing apps need granularity on order of radio
  range
• Smart spaces apps might need something in between

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Characteristics of Ranging Techniques

• Range data is the input to localization
  algorithms
• Error properties of different mechanisms vary
• Active acoustic ranging (radio synchronization)
• sub-cm accuracy for LOS, error independent of
  distance
• significant non-gaussian error term for NLOS
• Stereopsis
• angular error is a function of image sensor
  resolution
• Requires LOS. Detection errors non-gaussian
• RSSI
• Long-term averaging to counteract fading
• Depends on model of propagation characteristics
  (indoors?)

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Problems

• Problems
• Non-gaussian error terms
• Ambiguities and error caused by persistent
  environmental conditions (esp. for fixed nodes)
• Proposed Solutions
• Calibration by tracking a known track
• Cross-validation across different ranging modes

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Cross-Validation

• Consider several modes of ranging
• None of these mechanisms are perfect
• Many have non-gaussian error
• But errors may not be correlated
• Cross validation
• Detects ambiguities early on, and improves local
  estimates, with only local knowledge

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Power Considerations

• Current experiments use acoustics (and radio for
  time synchronization)
• Acoustic transmission is 1/3W x 2 sec for 20
  range measurements
• Receiver and detector is similar to a radio
  receiver
• Receiver use coordinated by radio
• Refresh rate corresponds to rate of change in
  network
• Moving objects might be tracked by passive
  detection

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Links Current Work

• Tiered Architectures
• A Cerpa, J Elson, D Estrin, L Girod, M Hamilton,
  J Zhao, Habitat monitoring Application driver
  for wireless communications technology 2001 ACM
  SIGCOMM Workshop on Data Communications in Latin
  America and the Caribbean, Costa Rica, April
  2001.
• Localization
• L Girod, D Estrin, "Robust Range Estimation Using
  Acoustic and Multimodal Sensing" In submission to
  IEEE/RSJ International Conference on Intelligent
  Robots and Systems (IROS 2001), Maui, Hawaii,
  October 2001.
• N Bulusu, J Heidemann, D Estrin, Adaptive Beacon
  Placement Proceedings of the Twenty First
  International Conference on Distributed Computing
  Systems (ICDCS-21), Phoenix, Arizona, April 2001.
• Time Synchronization
• J Elson, D Estrin, Time Synchronization for
  Wireless Sensor Networks Proceedings of the 2001
  International Parallel and Distributed Processing
  Symposium (IPDPS), Workshop on Parallel and
  Distributed Computing Issues in Wireless and
  Mobile Computing, San Francisco, California, USA.
  April 2001.
• Diffusion Routing
• C Intanagonwiwat, R Govindan, D Estrin, Directed
  Diffusion A Scalable and Robust Communication
  Paradigm for Sensor Networks In Proceedings of
  the Sixth Annual International Conference on
  Mobile Computing and Networks (MobiCOM 2000),
  August 2000, Boston, Massachusetts.
• http//lecs.cs.ucla.edu
• http//nesl.ee.ucla.edu/projects/ahlos/
• http//nesl.ee.ucla.edu/sensorsim/

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Coherent Processing Algorithms

• Phase information time series data shared among
  neighbors
• Benefits
• Localization of signals through beamforming
• Higher SNR
• Costs
• Tight time synchronization required
• High local communication cost
• Organizational issues
• Finding a fusion center
• Determining which nodes should participate
• Multi-hop time synchronization

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Distributed Power Management

• Shutdown
• Which nodes should shut down?
• How do they wake up when something interesting
  happens?
• Duty cycle
• Applies at many levels of system
• Radio MAC layer
• Processor/OS
• Application
• All layers must be power aware
• Wakeup
• Low power front end (sensors/DSP)
• Wakes up rest of node when event is detected

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The long term goal
Embed numerous distributed devices to monitor and
interact with physical world in work-spaces,
hospitals, homes, vehicles, and the environment
(water, soil, air)
Network these devices so that they can coordinate
to perform higher-level tasks. Requires robust
distributed systems of hundreds or thousands of
devices.