Embedding the Internet: This Century Challenges
Deborah Estrin
UCLA Computer Science Department
destrin@cs.ucla.edu
http://lecs.cs.ucla.edu/estrin
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Embedded Networked Sensing Potential
Seismic Structure
response
Marine
Microorganisms
• Micro-sensors, onboard 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
Contaminant
Transport
Ecosystems,
Biocomplexity
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Enabling Technologies
Embed numerous distributed
devices to monitor and interact
with physical world
Embedded
Network devices
to coordinate and perform
higher-level tasks
Networked
Exploit
collaborative
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…
Network SelfOrganization
Human
interface
Theoretical
framework
Programming
models
Node Localization
Communication
Links
Mobility and
navigation
System Energy
Management
Target Identification
Algorithms
Actuation
Sensors
Database policies
and architecture
Connection to
infrastructure
Cooperative Detection
Modeling of
Environment
Calibration
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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, resourcelimited, 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 result…local 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
Collaborative
Signal
Processing and
Active
Databases
Sensor
Coordinated
Actuation
Adaptive
Self-Configuration
Environmental
Microsensors
Academic Disciplines
Networking
Communications
Signal Processing
Databases
Embedded Systems
Controls
Optimization
…
Biology
Geology
Biochemistry
Structural Engineering
Education
Environmental Engineering
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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.edu/estrin
Or email to destrin@cs.ucla.edu
Recommended reading: NRC Report Embedded Everywhere
http://www4.nationalacademies.org/cpsma/cstb.nsf/web/pub_embedded
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