Constraint and Restoring Force
Long-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 C...
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2007
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| Online Access: | http://hdl.handle.net/1721.1/38484 |
| _version_ | 1811096051650658304 |
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| author | Beal, Jacob Bachrach, Jonathan Tobenkin, Mark |
| author2 | Gerald Sussman |
| author_facet | Gerald Sussman Beal, Jacob Bachrach, Jonathan Tobenkin, Mark |
| author_sort | Beal, Jacob |
| collection | MIT |
| description | Long-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 CRFs 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. |
| first_indexed | 2024-09-23T16:37:34Z |
| id | mit-1721.1/38484 |
| institution | Massachusetts Institute of Technology |
| last_indexed | 2024-09-23T16:37:34Z |
| publishDate | 2007 |
| record_format | dspace |
| spelling | mit-1721.1/384842019-04-12T08:38:35Z Constraint and Restoring Force Beal, Jacob Bachrach, Jonathan Tobenkin, Mark Gerald Sussman Mathematics and Computation amorphous computing spatial computing Proto Long-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 CRFs 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. 2007-08-27T14:23:35Z 2007-08-27T14:23:35Z 2007-08-24 MIT-CSAIL-TR-2007-044 http://hdl.handle.net/1721.1/38484 Massachusetts Institute of Technology Computer Science and Artificial Intelligence Laboratory 12 p. application/pdf application/postscript |
| spellingShingle | amorphous computing spatial computing Proto Beal, Jacob Bachrach, Jonathan Tobenkin, Mark Constraint and Restoring Force |
| title | Constraint and Restoring Force |
| title_full | Constraint and Restoring Force |
| title_fullStr | Constraint and Restoring Force |
| title_full_unstemmed | Constraint and Restoring Force |
| title_short | Constraint and Restoring Force |
| title_sort | constraint and restoring force |
| topic | amorphous computing spatial computing Proto |
| url | http://hdl.handle.net/1721.1/38484 |
| work_keys_str_mv | AT bealjacob constraintandrestoringforce AT bachrachjonathan constraintandrestoringforce AT tobenkinmark constraintandrestoringforce |