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Wireless Sensor Networks: Application Driver for Low

Power Systems

Deborah EstrinLaboratory 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)

Engineering: monitoring (and modeling) structures

www.jamesreserve.edu

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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

in-network 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 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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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

• 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

Target Trajectory

Target

Snapshots of previous Frisbees

Currently activesensor region

Inactive (sleeping) sensors

• Network nodes close to tracked event (or with good data on the event) enter fully active state; other nodes dormant/low duty cycle

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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 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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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, EverywhereA 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 self-

configuration, 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, Zhaohttp://www.isi.edu/scadds

– UCB Intel Lab: Culler, et.al.