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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

• 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

Marine Microorganisms

Ecosystems, Biocomplexity

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Enabling Technologies

Embedded Networked

Sensing

Control system w/Small form factorUntethered nodes

ExploitcollaborativeSensing, action

Tightly coupled to physical world

Embed numerous distributed devices to monitor and interact with physical world

Network devices to coordinate and perform higher-level tasks

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

Network Self-Network Self-OrganizationOrganization

Programming Programming modelsmodels

Database policiesDatabase policiesand architectureand architecture

SensorsSensors

Connection toConnection toinfrastructureinfrastructure

Cooperative DetectionCooperative DetectionCommunicationCommunication

LinksLinks

TheoreticalTheoreticalframeworkframework

Node LocalizationNode Localization

Mobility andMobility andnavigationnavigation

Target IdentificationTarget IdentificationAlgorithmsAlgorithms

System EnergySystem EnergyManagementManagement ActuationActuation

HumanHumaninterfaceinterface

Modeling ofModeling ofEnvironmentEnvironment

CalibrationCalibration

• 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 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

Collaborative Signal

Processing and Active

Databases

Adaptive Self-Configuration

Sensor Coordinated

Actuation

Environmental Microsensors

CENS Core Research Academic Disciplines

NetworkingCommunications

Signal ProcessingDatabases

Embedded SystemsControls

Optimization…

BiologyGeology

BiochemistryStructural Engineering

EducationEnvironmental Engineering

NetworkingCommunications

Signal ProcessingDatabases

Embedded SystemsControls

Optimization…

BiologyGeology

BiochemistryStructural Engineering

EducationEnvironmental 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 Everywherehttp://www4.nationalacademies.org/cpsma/cstb.nsf/web/pub_embedded