Wireless Sensor Networks: Application Driver for Low Power Systems

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Wireless Sensor Networks Application Driver for
Low Power Systems

• Deborah Estrin
• Laboratory for Embedded Collaborative Systems
  (LECS)
• UCLA Computer Science Department
• http//lecs.cs.ucla.edu destrin_at_cs.ucla.edu

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Applications
Scientific eco-physiology, biocomplexity mapping
Infrastructure contaminant flow monitoring (and
modeling)
www.jamesreserve.edu
Engineering monitoring (and modeling)
structures
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Common Vision

• Embed numerous distributed devices to monitor and
  interact with physical world
• Exploit spatially and temporally dense, in situ,
  sensing and actuation
• Network these devices so that they can
  coordinate to perform higher-level tasks
• Requires robust distributed systems of hundreds
  or thousands of devices

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Challenges

• Tight coupling to the physical world and embedded
  in unattended control systems
• Different from traditional Internet, PDA,
  Mobility applications that interface primarily
  and directly with human users
• Untethered, small form-factor, nodes present
  stringent energy constraints
• Living with small, finite, energy source is
  different from traditional fixed but reusable
  resources such as BW, CPU, Storage
• Communications is primary consumer of energy in
  this environment
• R4 drop off dictates exploiting localized
  communication and in-network processing whenever
  possible

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New Design Themes

• Long-lived systems that can be untethered and
  unattended
• Low-duty cycle operation with bounded latency
• Exploit redundancy
• Tiered architectures (mix of form/energy factors)
• Self configuring systems that can be deployed ad
  hoc
• Measure and adapt to unpredictable environment
• Exploit spatial diversity and density of
  sensor/actuator nodes

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Approach

• Leverage data processing inside the network
• Exploit computation near data to reduce
  communication
• Achieve desired global behavior with adaptive
  localized algorithms (i.e., do not rely on global
  interaction or information)
• Dynamic, messy (hard to model), environments
  preclude pre-configured behavior
• Cant afford to extract dynamic state information
  needed for centralized control or even
  Internet-style distributed control

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Why cant we simply adapt Internet protocols and
end to end architecture?

• Internet routes data using IP Addresses in
  Packets and Lookup tables in routers
• Humans get data by naming data to a search
  engine
• Many levels of indirection between name and IP
  address
• Works well for the Internet, and for support of
  Person-to-Person communication
• Embedded, energy-constrained (un-tethered,
  small-form-factor), unattended systems cant
  tolerate communication overhead of indirection

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Techniques for building long-lived

• Exploiting redundancy
• Adaptive Self-Configuration
• Supporting low-duty cycle operation
• Exploiting heterogeneity

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Exploiting Redundancy Goal

• To extend system lifetime
• We may be able to deploy 100 times as many nodes
  in environments where we cant increase the
  battery capacity by factor of 100
• To overcome environmental limitations
  (obstructions)
• Non line of site conditions, Variable sensor
  coupling
• To achieve good coverage with ad-hoc deployment
• When deployment or operational conditions cant be
  controlled precisely

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Exploiting Redundancy example

• Efficient, multi-hop topology formation goal
  exploit redundancy provided by high density to
  extend system lifetime while providing
  communication and sensing coverage.
• If too many sensors active at the same time,
  increase energy consumption and competition for
  communication resources.
• If too few nodes active, then lack of
  communication and/or sensing coverage.
• Central control/configuration requires too much
  communication
• Nodes should self-configure to find the right
  trade-off
• Ultimately should adapt based on desired
  fidelity

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Adaptive Fidelity Examples

• ASCENT
• Node measures number of neighbors and packet loss
  to determine participation, duty cycle, and/or
  power level.
• Ratio of energy used byActive case (all nodes
  turn on) to energy used by ASCENT
• GAF
• Uses Geographic information to infer which nodes
  might be redundant with one another for the
  purposes of routing
• Open question Can we apply Adaptive Fidelity
  etmore generally?

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• Ratio of energy used by the Active case (all
  nodes turn on) to the energy used by ASCENT
• ASCENT provides significant energy savings over
  the Active case

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Robustness and Scalability through Adaptation

• Adaptive mechanisms increase complexity but
  enable self-configuration for robustness and
  scalability
• Self calibration to adapt to variations in sensor
  response and placement
• Adjust duty cycle and transmit range as a
  function of node density and measured range
  (adaptive fidelity)
• Balance increased system life-time with increased
  resolution
• Challenge develop and evaluate localized
  adaptive algorithms
• We hope adaptive functions will go beyond
  connectivitye.g., tracking

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Supporting low duty cycle operation

• S-MAC
• A MAC designed for wireless sensor networks by
  increasing and facilitating sleep time and
  reducing overhearing and contention energy
  expenditure
• Triggering and tracking
• Use lower-power modalities, devices, to trigger
  higher power ones
• Use active devices to trigger sleeping devices to
  increase fidelity
• Paging channels

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Supporting low duty cycle operation

• S-MAC
• Message passing
• Periodic listen/sleep
• Avoid overhearing
• Energy Measurement
• On motes and TinyOS
• Two-hop network with 2 sources and 2 sinks
• Under different traffic load

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Adaptive Tracking Example

• Network nodes close to tracked event (or with
  good data on the event) enter fully active state
  other nodes dormant/low duty cycle

• Sentry nodes active wake up dormant nodes when
  necessary.
• Wakeup wavefront precedes phenomenon being
  tracked.
• Information driven diffusion (Zhao, Reich,
  et.al.) node propagates expression for
  evaluating best next node(s) in wavefront based
  on information utility and cost
• Requires
• low power operating mode with wake up/paging
  channel
• definition of a wakeup wavefront using localized
  algorithms
• time synchronization

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Low Duty Cycle Time Synchronization

• Pulse synchronization creates locality of
  synchronized nodes, quickly and efficiently
• External node generates pulse. Synchronizing
  nodes compare reception times.
• NTP good at correcting frequency
• Local pulse good at correcting phase
• Use combination

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Exploiting Heterogeneity Tiered Architecture

• Technological advances will never prevent the
  need to make tradeoffs
• Nodes will need to be faster or more
  energy-efficient, smaller or more capable or more
  durable.
• Tiered platform consisting of a heterogeneous
  collection of hardware.
• Larger, faster, and more expensive hardware
  (sensors)
• Smaller, cheaper, and more limited nodes (tags
  and motes)

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Tiered Architecture

• Discover and exploit asymmetry wherever possible
• Base stations for aggregating resources motes
  for access to physical phenomena
• Variable power, distance radios
• E.g., nodes in ASCENT can adapt by reducing their
  radio range, using less energy and reducing
  channel contention.
• Multiple modalities
• E.g., localization with RF, Acoustics, and Imaging

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Can we eliminate the finite nature of the energy
source?

• Batteries will provide 1J/mm3 (Pister)
• When available, solar has a lot (the most) to
  offer in recharging (Pister)
• Other possibilities Charging the batteries on
  fields of sensors by driving through them ?

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Current Research Areas

• Constructs for in network distributed
  processing
• system organized around naming data, not nodes
• Programming large collections of distributed
  elements
• Localized algorithms that achieve system-wide
  properties
• Time and location synchronization
• energy-efficient techniques for associating time
  and spatial coordinates with data to support
  collaborative processing
• Experimental infrastructure

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Current COTS Infrastructure
PC-104(off-the-shelf)
UCB Mote (Culler/Hill/Pister)

• Software
• Directed Diffusion
• TinyOS (UCB/Culler)
• Measurement, Simulation

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Embedded, EverywhereA Research Agenda for
Networked Systems of Embedded Computers

• Fall 2001 Computer Science and
  Telecommunications Board report (late September)
• Recommends major areas of research needed to
  achieve robust, scalable EmNets
• predictability, adaptive self-configuration,
  monitoring system health, computational models,
  network geometry, interoperability, social and
  policy issues
• Substantive recommendations to DARPA, NIST, NSF

For more information, see www.cstb.org or contact
lmillett_at_nas.edu
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Future Directions

• Proposed Center for Embedded Networked Sensing
  (CENS)
• Develop technology architecture, software,
  components in the context of driving application
  prototypes
• Habitat monitoring/Biocomplexity mapping
• Seismic activity and structure response
• Contaminant flow monitoring
• Grades 7-12 science curricula innovations

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Acknowledgments

• Funders
• DARPA SenseIT and NEST Programshttp//www.darpa.m
  il/ito/research/sensit
• NSF Special Projects
• Cisco, Intel
• Collaborators
• UCLA LECS students Bien, Bulusu, Busek,
  Braginsky, Bychkovskiy, Cerpa, Elson, Ganesan,
  Girod, Greenstein, Perelyubskiy, Scoellhammer, Yu
  http/lecs.cs.ucla.edu/
• USC-ISI Collaborators Govindan, Heidemann,
  Intanago, Silva, Wei, Zhaohttp//www.isi.edu/scad
  ds
• UCB Intel Lab Culler, et.al.