CONSONA Constraint Networks for the Synthesis of Networked Applications

1. CONSONAConstraint Networks for the Synthesisof Networked Applications Lambert Meertens Asuman Sünbül Stephen Fitzpatrick Matthias Anlauff http://consona.kestrel.edu/ NEST PI Meeting San Francisco, CA, July 15–16, 2003 Lambert Meertens Cordell Green Kestrel Institute

2. Project Objective • TheConsonaprojectaimsatdevelopingtrulyscalable fine-grain fusion of physical and information processes in large ensembles of networked nodes • In particular we’re interested in cases where the networked system can be viewed as a discrete approximation of computation on a continuous field • Large-scale fine-grain systems have many potentially serious bottlenecks, and the design process must allow exploring trade-offs

3. Overview of Technical Approach • Services and applications are modeled as 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 provided that ancillary constraints S are maintained, executing code M maintains constraint C • High-level code is optimized to generate efficient low-level code

4. Continuous Soft Constraints General Approach • System of soft constraints on a set xof real ‘unknowns’: y0, where y f(x) for some smooth vector-valued function f • Exact equality y=0is typically unattainable • Instead, try to minimize yTy (‘Least Squares’): scalar yTy = 0 precisely when y=0exactly • Under (e.g.) solution uniqueness, solution of system yTyx=0, where yx is the matrix of partial derivatives of y with respect to the variables in x, guarantees minimality of yTy • Solve this system

5. Continuous Soft Constraints ctd. Overcoming Computational Complexity • For large numbers of motes the number of unknowns may be large and direct solution unfeasible • Instead, partition the problem so that each mote is ‘the responsible agent’ for only a few unknowns (not: constraints) • Each agent tries to minimize yTy for ‘its’ unknowns and disseminates the results, using disseminated estimates for the values of the other unknowns • Repeat as needed

6. –  0 2 2 2 2 (dij–dik) –(Dij–Dik) 0 2 dij 2 Dij Localization as Constraints (1) Local Map Formation • Unknowns: xiand yifor i ranging over the participating motes • Assume measured distances Dij are known • Constraints: dij–Dij 0, where dij is the Euclidean distance between (xi,yi) and (xj,yj) Transforming the Constraints • Constraints: • Constraints: • This is a linear ‘soft’ equation in xiand yi!

7. Localization as Constraints (2) Solving the Constraints • For locating mote i, we have one soft constraint for all (unordered) pairs of nodes to which it has a known distance estimate • These are not all independent, but enough to determine the location of a 3-connected mote, unless the other motes are all collinear • Now solve as before: the final system has just two linear equations in two unknowns • A ‘starter’ is needed: first construct a map for a triangle with known side lengths

8. T(xi,yi) – (xi,yi)  0 ' ' ' ' (xi,yi) Localization as Constraints (3) Reconciling Local Maps of Different Motes • High-level variant of Sense-Fuse-Disseminate paradigm • Constraint: for some isometric transformation T, for motes i that appear on both maps • Two-step process: first solve for (unknown parameters of) T; next average T(xi,yi) and

9. Imposing One Color / System / … • Each mote i has a random number 0  ri < 1 • Mote broadcastsits ‘color’ with ri to neighbors • Neighbor j adopts ‘color’ and ri if ri< rj

10. Strengths of Localization Method Expected strengths, have to be borne out by experiments with real motes • Does not require anchor motes — but can use them if available • Can be used in anytime mode IFranging can be squeezed in as ongoing background activity • Lowered sensitivity to ‘noise’, especially in combination with simple outlier detection and rejection • Modest memory use

11. Weaknesses of Localization Method • Depends on heavy-duty ranging (but only required in initialization phase for stationary motes) • Patching together of local maps gives ‘flabby’ global map, even if soft constraint dij–Dij 0 is satisfied quite well — alas unavoidable for all methods constructing a map from perturbed local distance data • If long-range ranging is possible, problem can be circumvented by multi-level approach: use map from higher level as scaffolding for next level

12. Development Status • Works in simulated environment for large numbers of nodes • Coded in nesC, compiles, runs, and sometimes produces promising results but needs further work — some “Can’t Happen’s” do • About 1200 LOC for actual algorithms, plus say 2000 LOC for inter-mote communication (data collection and dissemination) • Can this use Neighborhood service? Needs to be looked into

13. 300 Motes, No Noise

14. 200 Motes With Noise

15. Kestrel’s Simulator Extensions • Based on Berkeley’s Nido • Simulation of microphone and sounder (not used in experiments, physical model for sensitivity, delays etc. not validated) • Simulation of the VU Acoustic Ranging component (used for Kestrel’s work on localization, validated for MICA2 by similar results for TestAcousticRanging as on hardware motes) • Simulation of magnetometer (used for simulation of ‘MagTrackingDemo’ application)

16. Microphone/Sounder Simulation • Kestrel has extended the graphical interface to the Nido simulator (TinyViz) that it visualizes and animates microphone and sounder events. • It communicates with the SMSIM extension to the TinyOS simulator using the plug-in mechanism provided by the TinyViz framework. • The microphone/sounder plug-in provides the following functionality: • Assignment of mote locations • Animation of microphone and sounder events

17. Microphone/Sounder Simulation ctd. • Quick start documentation/installation guide available at consona.kestrel.edu • Implementation is explained by a walk-through scenario for a given mote emitting a sound at a given time • Documentation describes how tone detection events are created in the neighboring motes

19. Simulation of VU Acoustic Ranging • The VU Ranging component is used to determine distances between motes by using the difference between time-of-flight for a radio signal and a sound signal • The motes emit once every minute a radio signal and a chirping sound at the same time, so that receiving motes can estimate the distance to the sender from the time elapsed between the times of arrival of the two signals • We do not simulate the actual algorithm; instead, the simulated component provides the same interface and similar behavior to its ‘users’

21. Simulation of MagTrackingDemo • Extension of Nido for simulating the MagTrackingDemo application • Magnetometer sensor simulation • analyzed real sensor values • developed a model of a magnetometer • implemented a replacement component for the magnetometer in platforms/pc • Documentation and implementation guide at consona.kestrel.edu

23. Toolset: Modeler / Generator • Modeler for Constraint Decomposition • various minor improvements • not used for present work on Localization: requires advanced symbolic algebra capabilities • Consona Code Generator • work on adapting code generation from TOS C to nesC not yet in implementation phase — priority given to work on Localization

24. Goals and Success Criteria • Localization service • scalability: rate of increase in computational, storage and communication costs with number of motes • accuracy: error in position estimates as a function of error in distance estimates • Code generation • compactness of abstract code vs. compactness of nesC code

25. Project Plans • Investigate how localization service can be adapted to non-Gaussian noise in distance estimates • e.g., extend outlier rejection methods • e.g., use multi-hypothesis techniques for initial map construction by individual motes (before inter-mote reconcilliation) • Adapt code generator to nesC • Integrate modeler with generator