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Lambert Meertens & Cordell Green

Kestrel Institute

Consona Constraint Networks for the Synthesis of

Networked Applications

Refinement of aSense-Fuse-Disseminate Paradigm for

Scalable Sensor Networks

Asuman SünbülMatthias Anlauff

Stephen Fitzpatrick

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Overview of Project

• Software focus– use the motes as given– would like to be able to use other types of hardware

• Develop model-based methods and tools that– integrate design and code generation

design-time performance trade-offs

– in a goal-oriented way goal-oriented run-time performance trade-offs

– of NEST applications and services low composition overhead

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Overview of Technical Approach

• Both services and applications are modeled as sets of soft constraints, to be maintained at run-time

• High-level code is produced by repeated instantiation of constraint-maintenance schemas– Constraint-maintenance schemas are represented

as triples (C, M, S), meaning that• constraint C can be maintained by

• running code M,

• provided that ancillary constraints S are maintained

• High-level code is optimized to generate efficient low-level code

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Overview of Demonstration

• Constraint-based specification of tracking application

• Schema-based refinement into high-level code– assumes coordinate system

• Synthesis of low-level code– reality check: simplified algorithm

• Code in action

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Application

• Track a moving target– solution must be scalable & robust

• For simplicity, use photocell– target carries a standardized light source– target-mote distance estimated from

photocell reading– could use any sensor that provides a

reliable distance estimate• RF, acoustic found to be unreliable

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Specification

• Top level specification:– maintain an estimate of the target’s position

• Mote-level specification:– each mote maintains an estimate (est) of the

target’s position– Constraint: FieldConsistent(est)

• the estimates must agree with each other

– Constraint: SensorConsistent(photocell, est)• the estimates must agree with the sensors

– scalable specification/requirement — local coupling

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Refinement: Field Consistent

i:mote· FieldConsistent(x) j:neighbors(i)·

EdgeConsistent(i.x, j.x)• neighbors(i, j)

EdgeConsistent(i.x, j.x) diffuse(x)

• code diffuse(x) {on tick do broadcast(x);on receive(x) do smooth(x, x)

}• scalable, local interaction

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Refinement: Sensor Consistent

• SensorConsistent(S, x) sense(S, x)

• code sense(S, x) {on tick do fuse(S, x)

}

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Refinement: Estimates

• Target Estimate = 2D rotated Gaussian– represented as quintuple <xc, yc, u, v, w>

– p(x, y) = Kּexp(-Q(x-xc, y-yc)/2)

– where Q(a,b) = uּa2 + vּaּb + wּb2

– K = 1/sqrt(uּw-v2)

x

y

xc, yc

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Refinement: Smoothing

• Smoothing is weighted product– smooth(e, f) = e(1-) ּf

– cheap to compute using logs under transformed coordinates

• 5 floating point additions

• 2D rotated Gaussians are closed under product

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Refinement: Fuse

• To fuse a photocell reading into a position estimate– deduce a distance estimate (ring) from the

photocell reading– interpolation over calibration table

– approximate the product of the original estimate and reading’s estimate

– not closed: use 2D rotated Gaussian that is a maximum likelihood estimator

• same means and first moments (approx.)d

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High-Level Code

• High-level code represented as e-Specs– “practical category theory for motes”– state machines with strong semantics defining each

state and transition– hope: common abstraction for Berkeley & Boeing

OEPS

• Well-suited to representing single-mote modules/algorithms– composition & refinement– optimization at code level

• Low-level C code is automatically synthesized

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Simplified Algorithm: Trilateralization

• Need simplified algorithm for today’s demo– still getting acquainted with TOS/C

• Motes periodically broadcast distance estimates

• Motes periodically estimate new target position using (approximate) trilateralization– and smooth with old target estimate

d1

d3

d2

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Demonstration

• Live– e-Specs– code synthesis– tracking

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Evaluation Criteria: Qualitative

• Field Consistent & Sensor Consistent– are useful, intuitive constraints for

specifying applications in scalable sensor networks

• Incremental constraint maintenance / optimization– through perpetual smoothing & fusion is a

useful coding paradigm for scalable sensor networks

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Evaluation Criteria: Quantitative

• Accuracy of estimates– most easily measured with static targets– value: ~6 inches

• outliers more extreme

• Communication requirements– number of messages per mote per second– value: 2

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Demonstration Issues

• TOS software is poorly documented– circuit diagrams are of little value to

software engineers (=me)

• Communication range is low when sensor boards are added

• For large scale experiments:– field programmable motes would be nice– faithful sensor simulators would be nice