Embedding the Internet: This Century Challenges

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Embedding the Internet This Century Challenges

• Deborah Estrin
• UCLA Computer Science Department
• destrin_at_cs.ucla.edu
• http//lecs.cs.ucla.edu/estrin

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Embedded Networked Sensing Potential

• Micro-sensors, on-board processing, and wireless
  interfaces all feasible at very small scale
• can monitor phenomena up close
• Will enable spatially and temporally dense
  environmental monitoring
• Embedded Networked Sensing will reveal previously
  unobservable phenomena

Seismic Structure response
Contaminant Transport
Ecosystems, Biocomplexity
Marine Microorganisms
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Enabling Technologies
Embed numerous distributed devices to monitor and
interact with physical world
Network devices to coordinate and perform
higher-level tasks
Embedded
Networked
Exploitcollaborative Sensing, action
Control system w/ Small form factor Untethered
nodes
Sensing
Tightly coupled to physical world
Exploit spatially and temporally dense, in situ,
sensing and actuation
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• The network is the sensor (Oakridge National
  Labs)
• Requires robust distributed systems of thousands
  of physically-embedded, often untethered,
  devices.
• Technical Challenges
• Energy constraints imposed by unattended,
  untethered, micro-scale systems.
• Level of dynamics ( Environmental obstacles,
  weather, terrain System large number of nodes,
  failures.)
• Scaling challenges due to very large numbers of
  distributed nodes.

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New Design Themes

• Massively distributed, untethered, and unattended
  systems to cover spatially distributed phenomena
  in natural, obstructed, environments
• In-network procesing
• Thousands or millions of operations per second
  can be done using energy of sending a bit over 10
  or 100 meters (Pottie00)
• Exploit computation near data sources to reduce
  communication
• Self configuring systems that can be deployed ad
  hoc
• Un-modeled dynamics of physical world cause
  systems to operate in ad hoc fashion
• Measure and adapt to unpredictable environment
• Exploit spatial diversity and density
  (redundancy) of sensor/actuator nodes
• Adaptive localized algorithms to achieve desired
  global behavior
• 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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From Embedded Sensing to Embedded Control

• Embedded in unattended control systems
• Different from traditional Internet, PDA,
  Mobility applications that interface primarily
  and directly with human users
• More than control of the sensor network itself
• Critical applications extend beyond sensing to
  control and actuation
• Transportation, Precision Agriculture, Medical
  monitoring and drug delivery, Battlefied
  applications
• Critical concerns extend beyond traditional
  networked systems
• Usability, Reliability, Safety
• Robust interacting systems under dynamic
  operating conditions
• Often mobile, uncontrolled environment,
• Not amenable to real-time human monitoring
• Need systems architecture to manage interactions
• Current system development one-off,
  incrementally tuned, stove-piped
• Serious repercussions for piecemeal uncoordinated
  design insufficient longevity, interoperability,
  safety, robustness, scalability...

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ENS Research Focus

• Algorithms, architecture, reference
  implementations, to achieve distributed,
  in-network, autonomous event detection
  capabilities
• Strive toward an Architecture and associated
  principles
• Develop working systems and extract reusable
  building blocks
• Analogous to TCP/IP stack, soft state, fate
  sharing, and eventually, self-similarity,
  congestion control

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Enabling Technologies

• Microsensors and actuators
• Low power wireless and media access
• Integrated, small form factor, devices
• Software
• Interfaces
• Smart dust
• Tiered architectures
• Time and location synchronization
• See presentations by Culler, Goldsmith, Mitra,
  Pister

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Adaptive Self-Organization

• Goal achieve reliable, long-lived, operation in
  dynamic, resource-limited, harsh environment.
• Adapt
• Topology to achieve efficient communciation,
  sensing, processing, or dissemination coverage
  (may be application and data driven)
• Aggregation/processing points to achieve
  efficient compression
• How well can we do with localized algorithms that
  do not rely on centralized control or global
  knowledge ?
• Metrics system lifetime, quality of detection
• Models and metaphors from biology and physics
• See presentations by Albert, Doyle, Francescheti,
  Goldsmith, Krishnamachari, Kumar

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Collaborative, multi-modal, processing

• In network processing must extend beyond signal
  processing, on a single node
• Collaborative signal processing
• Localization
• Compression
• Supression of redundant detections
• Sensor fusion
• See presentations by Effros, Potkonjak, Pottie,
  Ramachandran, Zhao

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Sensor coordinated actuation

• Actuation needed for fully self-configuring and
  reconfiguring systems
• Allow for adaptation in physical space
• Services provided
• Energy delivery
• Calibration
• Localization
• Sample collection
• Node placement
• Static sensors can assist mobile elements with
  navigation, search, coordination
• See presentations by Hogg, Sukhatme

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Primitives for Programming the Collective

• How do we task a 1000 node dynamic sensor
  network to conduct complex, long-lived queries
  and tasks ??
• Map isotherms and other contours, gradients,
  regions
• Nested behaviors to identify multi-parameter
  events
• Record images or mobilize robotic sample
  collection in response to event detection.
• See presentations by Culler, Sukhatme

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Safety, Predictability, Usability

• As we embed sophisticated behaviors in
  previously-simple objects.
• Support effective mental models that allow for
  correct interactions, adaptations, diagnosis
• Design themes
• Achieve isolation
• Constrain interactions
• See presentations at some future workshop

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Towards a Unified Framework for ENS

• General theory of massively distributed systems
  that interface with the physical world
• low power/untethered systems, scaling,
  heterogeneity, unattended operation, adaptation
  to varying environments
• Understanding and designing for the collective
• Local-global (global properties that resultlocal
  behaviors that support)
• Programming model for instantiating local
  behavior and adaptation
• Abstractions and interfaces that do not preclude
  efficiency
• Large-scale experiments to challenge assumptions
  behind heuristics

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Pulling it all together
CENS Core Research
Academic Disciplines
Networking Communications Signal
Processing Databases Embedded Systems Controls Opt
imization Biology Geology Biochemistry Structura
l Engineering Education Environmental Engineering
Adaptive Self-Configuration
Collaborative Signal Processing and Active
Databases
Experimental Systems
Sensor Coordinated Actuation
Environmental Microsensors
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Future Directions

• Tremendous opportunities for expanding research
  on horizon
• Driven from bottom up by sensor development
  (e.g., BioMEMS)
• Pulled from the top by emerging applications
  (e.g., medical, space exploration)
• Critical Concerns Security, Privacy, and Safety
• ENS systems in human environments will greatly
  alter human experience and intensify design
  requirements
• For further information see http//lecs.cs.ucla.ed
  u/estrin
• Or email to destrin_at_cs.ucla.edu
• Recommended reading NRC Report Embedded
  Everywherehttp//www4.nationalacademies.org/cpsma
  /cstb.nsf/web/pub_embedded