Techniques for Building Long-Lived Wireless Sensor Networks

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Techniques for Building Long-Lived Wireless
Sensor Networks

• Jeremy Elson and Deborah Estrin
• UCLA Computer Science Department
• And
• USC/Information Sciences Institute
• Collaborative work with R. Govindan, J.
  Heidemann, and SCADDS of other grad students

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What might make systems long-lived?

• Consider energy the scarce system resource
• Minimize communication (esp. over long distances)
• Computation costs much less, so
• In-network processing aggregation, summarization
• Adaptivity at fine and coarse granularity
• Maximize lifetime of system, not individual nodes
• Exploit redundancy design for low duty-cycle
  operation
• Exploit non-uniformities when you have them
• Tiered architecture
• New metrics

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What might make systems long-lived?

• Robustness to dynamic conditions Make system
  self-configuring and self-reconfiguring
• Avoid manual configuration
• Empirical adaptation (measure and act)
• Localized algorithms prevent single points of
  failure and help to isolate scope of faults
• Also crucial for scaling purposes!

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The Rest of the Talk

• Some of our initial building blocks for creating
  long-lived systems
• Directed diffusion - a new data dissemination
  paradigm
• Adaptive fidelity
• Use of small, randomized identifiers
• Tiered architecture
• Time synchronization

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Directed DiffusionA Paradigm for Data
Dissemination

• Key features
• name data, not nodes
• interactions are localized
• data can be aggregated or processed within the
  network
• network empirically adapts to best distribution
  path, the correct duty cycle, etc.

1. Low data rate
2. Reinforcement
3. High data rate
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Diffusion Key Results

• Directed diffusion
• Can provide significantly longer network
  lifetimes than existing schemes
• Keys to achieving this
• In-network aggregation
• Empirical adaptation to path
• Localized algorithms and adaptive fidelity
• There exist simple, localized algorithms that can
  adapt their duty cycle
• they can increase overall network lifetime

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Adaptivity I Robustness in Data Diffusion
A primary goal of data diffusion is robustness
through empirical adaptation measuring and
reacting to the environment.
20 node failure
Because of this adaptation, mean latency (shown
here) for data diffusion degrades only
mildly even with 10-20 node failure.
10 node failure
no failures
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Adaptivity IIAdaptive Fidelity

• extend system lifetime while maintaining accuracy
• approach
• estimate node density needed for desired quality
• automatically adapt to variations in current
  density due to uneven deployment or node failure
• assumes dense initial deployment or additional
  node deployment

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Adaptive Fidelity Status

• applications
• maintain consistent latency or bandwidth in
  multihop communication
• maintain consistent sensor vigilance
• status
• probablistic neighborhood estimation for ad hoc
  routing
• 30-55 longer lifetime with 2-6sec higher initial
  delay
• currently underway location-aware neighborhood
  estimation

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Small, Random Identifiers

• Sensor nets have many uses for unique
  identifiers(packet fragmentation, reinforcement,
  compression codebooks...)
• Its critical to maximize usefulness of every bit
  transmitted each reduces net lifetime (Pottie)
• Low data rates high dynamics no space to
  amortize large (guaranteed unique) ids or
  claim/collide protocol
• So use small, random, ephemeral transaction ids?
• Locality is key random ids much smaller than
  guaranteed unique ids if total net size large and
  transaction density small
• ID collisions lead to occasional losses
  persistent losses avoided because the identifiers
  are constantly changing
• Marginal cost of occasional losses is small
  compared to losses from dynamics, wireless
  conditions, collisions

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Address-Free Fragmentation
AFF Allows us to optimize bits used for
identifiers Fewer bits fewer wasted bits per
data bit, but high collision rate vs.
More bits less waste due to ID
collisions but
many bits wasted on headers
Data Size16 bits
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Exploit Non-Uniformities ITiered Architecture

• Consider a memory hierarchy registers, cache,
  main memory, swap space on disk
• Due to locality, provides the illusion of a flat
  memory that has speed of registers but size
  price of disk space
• Similar goal in sensor nets we want a spectrum
  of hardware within a network with the illusion of
• CPU/memory, range, scaling properties of large
  nodes
• Price, numbers, power consumption, proximity to
  physical phenomena of the smallest

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Exploit Non-Uniformities ITiered Architecture

• We are implementing a sensor net hierarchy
  PC-104s, tags, motes, ephemeral one-shot sensors
• Save energy by
• Running the lower power and more numerous nodes
  at higher duty cycles than larger ones
• Having low-power pre-processors activate higher
  power nodes or components (Sensoria approach)
• Components within a node can be tiered too
• Our tags are a stack of loosely coupled boards
• Interrupts active high-energy assets only on
  demand

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Exploit Non-Uniformities IITime Synchronization

• Time sync is critical at many layers some affect
  energy use/system lifetime
• TDMA guard bands
• Data aggregation caching
• Localization
• But time sync needs are non-uniform
• Precision
• Lifetime
• Scope Availability
• Cost and form factor
• No single method optimal on all axes

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Exploit Non-Uniformities IITime Synchronization

• Use multiple modes
• Post-facto synchronization pulse
• NTP
• GPS, WWVB
• Relative time chaining
• Combinations can (?) be necessary and sufficient,
  to minimize resource waste
• Dont spend energy to get better sync than app
  needs
• Work in progress

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Conclusions

• Many promising building blocks exist, but
• Long-lived often means highly vertically
  integrated and application-specific
• Traditional layering often not possible
• Challenge is creating reusable components common
  across systems
• Create general-purpose tools for building
  networks, not general purpose networks