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@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 innetwork 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 Internetstyle 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 can’t 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 by
Active 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
“connectivity”…e.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 systemwide 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, Everywhere
A 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 selfconfiguration, 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@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 Programs
http://www.darpa.mil/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, Zhao
http://www.isi.edu/scadds
– UCB Intel Lab: Culler, et.al.
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