Difference between revisions of "Koushanfar2003"
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|abstract=We address the problem of energy efficient sensing by adaptively coordinating the sleep schedules of sensor nodes while guaranteeing that values of sleeping nodes can be recovered from the awake nodes within a user{\textquoteright}s specified error bound. Our approach has two phases. First, development of models for predicting measurement of one sensor using data from other sensors. Second, creation of the maximal number of subgroups of disjoint nodes, each of whose data is sufficient to recover the measurements of the entire sensor network. For prediction of the sensor measurements, we introduce a new optimal non-parametric polynomial time isotonic regression. Utilizing the prediction models, the sleeping coordination problem is abstracted to a domatic number problem and is optimally solved using an ILP solver. To capture evolving dynamics of the instrumented environment, we monitor the prediction errors occasionally to trigger adaptation of the models and domatic partitions as needed. Experimental evaluations on traces of a medium size network with temperature and humidity sensors indicate that the method can extend the lifetime of the network by a factor of 4 or higher even for a strict error target. | |abstract=We address the problem of energy efficient sensing by adaptively coordinating the sleep schedules of sensor nodes while guaranteeing that values of sleeping nodes can be recovered from the awake nodes within a user{\textquoteright}s specified error bound. Our approach has two phases. First, development of models for predicting measurement of one sensor using data from other sensors. Second, creation of the maximal number of subgroups of disjoint nodes, each of whose data is sufficient to recover the measurements of the entire sensor network. For prediction of the sensor measurements, we introduce a new optimal non-parametric polynomial time isotonic regression. Utilizing the prediction models, the sleeping coordination problem is abstracted to a domatic number problem and is optimally solved using an ILP solver. To capture evolving dynamics of the instrumented environment, we monitor the prediction errors occasionally to trigger adaptation of the models and domatic partitions as needed. Experimental evaluations on traces of a medium size network with temperature and humidity sensors indicate that the method can extend the lifetime of the network by a factor of 4 or higher even for a strict error target. | ||
|pages=475 - 480 | |pages=475 - 480 | ||
|month= | |||
|year=2003 | |||
|booktitle=ACM International Symposium on Low Power Electronics and Design (ISLPED) | |booktitle=ACM International Symposium on Low Power Electronics and Design (ISLPED) | ||
|title=Sleeping Coordination for Comprehensive Sensing Using Isotonic Regression and Domatic Partitions | |title=Sleeping Coordination for Comprehensive Sensing Using Isotonic Regression and Domatic Partitions | ||
|entry=inproceedings | |entry=inproceedings | ||
}} | }} | ||
Revision as of 04:28, 4 September 2021
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| author | F. Koushanfar and A. Davare and D. Nguyen and M. Potkonjak and A. Sangiovanni-Vincentelli |
| booktitle | ACM International Symposium on Low Power Electronics and Design (ISLPED) |
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| pages | 475 - 480 |
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| title | Sleeping Coordination for Comprehensive Sensing Using Isotonic Regression and Domatic Partitions |
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| year | 2003 |
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Email:
farinaz@ucsd.edu
farinaz@ucsd.edu
Address:
Electrical & Computer Engineering
University of California, San Diego
9500 Gilman Drive, MC 0407
Jacobs Hall, Room 6401
La Jolla, CA 92093-0407
Electrical & Computer Engineering
University of California, San Diego
9500 Gilman Drive, MC 0407
Jacobs Hall, Room 6401
La Jolla, CA 92093-0407
Lab Location: EBU1-2514
University of California San Diego
9500 Gilman Dr, La Jolla, CA 92093
University of California San Diego
9500 Gilman Dr, La Jolla, CA 92093