2009年2月15日 星期日

Ultra-low power data storage for sensor networks

Source Information Processing In Sensor Networks archive
Proceedings of the 5th international conference on Information processing in sensor networks table of contents
Nashville, Tennessee, USA
POSTER SESSION: SPOTS track table of contents
Pages: 374 - 381
Year of Publication: 2006
ISBN:1-59593-334-4
Authors
Gaurav Mathur University of Massachusetts, Amherst, MA
Peter Desnoyers University of Massachusetts, Amherst, MA
Deepak Ganesan University of Massachusetts, Amherst, MA
Prashant Shenoy University of Massachusetts, Amherst, MA

ABSTRACT
Local storage is required in many sensor network applications, both for archival of detailed event information, as well as to overcome sensor platform memory constraints. While extensive measurement studies have been performed to highlight the trade-off between computation and communication in sensor networks, the role of storage has received little attention. The storage subsystems on currently available sensor platforms have not exploited technology trends, and consequently the energy cost of storage on these platforms is as high as that of communication. Current flash memories, however, offer a low-priced, high-capacity and extremely energy-efficient storage solution.In this paper, we perform a comprehensive evaluation of the active and sleep-mode energy consumption of available flash-based storage options for sensor platforms. Our results demonstrate more than a 100-fold decrease in per-byte energy consumption for surface-mount parallel NAND flash in comparison with the MicaZ on-board serial flash. In addition, this dramatically reduces storage energy costs relative to communication, introducing a new dimension in traditional computation vs communication trade-offs. Our results have significant ramifications on the design of sensor platforms as well as on the energy consumption of sensing applications. We quantify the potential energy gains for two commonly used sensor network services: communication and in-network data aggregation. Our measurements show significant improvements in each service: 50-fold and up to 10-fold reductions in energy for communication and data aggregation respectively.

http://none.cs.umass.edu/papers/pdf/IPSN_SPOTS06.pdf

沒有留言:

張貼留言