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dc.contributor.advisorGerald Sussman
dc.contributor.authorBeal, Jacob
dc.contributor.authorBachrach, Jonathan
dc.contributor.authorTobenkin, Mark
dc.contributor.otherMathematics and Computation
dc.date.accessioned2007-08-27T14:23:35Z
dc.date.available2007-08-27T14:23:35Z
dc.date.issued2007-08-24
dc.identifier.otherMIT-CSAIL-TR-2007-044
dc.identifier.urihttp://hdl.handle.net/1721.1/38484
dc.description.abstractLong-lived sensor network applications must be able to self-repair and adapt to changing demands. We introduce a new approach for doing so: Constraint and Restoring Force. CRF is a physics-inspired framework for computing scalar fields across a sensor network with occasional changes. We illustrate CRF’s usefulness by applying it to gradients, a common building block for sensor network systems. The resulting algorithm, CRF-Gradient, determines locally when to self-repair and when to stop and save energy. CRF-Gradient is self-stabilizing, converges in O(diameter) time, and has been verified experimentally in simulation and on a network of Mica2 motes. Finally we show how CRF can be applied to other algorithms as well, such as the calculation of probability fields.
dc.format.extent12 p.
dc.relation.ispartofseriesMassachusetts Institute of Technology Computer Science and Artificial Intelligence Laboratory
dc.subjectamorphous computing
dc.subjectspatial computing
dc.subjectProto
dc.titleConstraint and Restoring Force


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