Comm n Sense: Research Challenges in Embedded Networked Sensing

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Comm n Sense Research Challenges in Embedded
Networked Sensing

• 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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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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Applications
Scientific eco-physiology, biocomplexity mapping
Infrastructure Contaminant flow monitoring
www.jamesreserve.edu
Engineering adaptive structures
Model Development
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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
• Sending a bit over 10 or 100 meters consumes as
  much energy as thousands/millions of operations
  (R4 signal energy drop-off Pottie-etal00).

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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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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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Constructs for in network processing

• Nodes pull, push, and store named data (using
  tuple space) to create efficient processing
  points in the network
• e.g. duplicate suppression, aggregation,
  correlation
• Nested queries reduce overhead relative to edge
  processing
• Complex queries support collaborative signal
  processing
• propagate function describing desired
  locations/nodes/data (e.g. ellipse for
  tracking)

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Self-Organization with Localized Algorithms

• Self-configuration and reconfiguration essential
  to lifetime of unattended systems in dynamic,
  constrained energy, environment
• Efficient, multi-hop topology formation node
  measures neighborhood to determine participation,
  duty cycle, and/or power level
• Beacon placement candidate beacon measures
  potential reduction in localization error
• Requires large solution space not seeking unique
  optimal
• Investigating applicability, convergence, role of
  selective global information

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Time and Location Synchronization

• Common time coordinate for in situ processing,
  correlation of events
• Developing methods that balance communication
  (energy) cost with other variables (e.g.,
  precision, scope,lifetime, cost, form factor)
• Post facto pulse synchronization
• Common spatial coordinate for 3-space related
  tasks and network operation (e.g., geo-routing)
• Methods that do not rely on GPS or RF RSSI (due
  to environment)
• Multi-modal localization using acoustic time of
  flight measurements, RF synchronization, and
  imaging to identify bad data sources (NLOS)

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Experimental Infrastructure
PC-104(off-the-shelf)
UCB Mote (Pister/Culler)

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

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Future Directions

• GALORE project
• Exploit locally-regular sub-structures
• Explore interaction of adaptive reconfiguration
  at multiple levels
• application software
• central processing hardware (FPGA)
• distributed sensor pre-processing network
• Center for Embedded Networked Sensing
• Habitat monitoring/Biocomplexity mapping
• Seismic activity and structure response
• Contaminant flow monitoring
• Grades 7-12 science curricula innovations

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References and Acknowledgments

• DARPA SenseIT and NEST Programshttp//www.darpa.m
  il/ito/research/sensit
• NSF Special Projects
• Cisco, Intel
• USC-ISI Collaborators Govindan, Heidemann,
  Silvahttp//www.isi.edu/scadds
• UCLA LECS members Bien, Bulusu, Braginsky,
  Bychkovskiy, Cerpa, Elson, Ganesan, Girod,
  Greenstein, Perelyubskiy, Yu, http//lecs.cs.ucla
  .edu