Evaluation of dynamic profiling methodologies for optimization of sensor networks

Ashish Shenoy; Jeff Hiner; Susan Lysecky; Roman Lysecky; Ann Gordon-Ross

doi:10.1109/LES.2010.2045634

Evaluation of dynamic profiling methodologies for optimization of sensor networks

Ashish Shenoy, Jeff Hiner, Susan Lysecky, Roman Lysecky, Ann Gordon-Ross

Research output: Contribution to journal › Article › peer-review

9 Scopus citations

Abstract

To reduce the complexity associated with application-specific tuning of sensor-based systems, dynamic profiling enables an accurate view of the application behavior, such that the network can be reoptimized at runtime in response to changing application behavior or environmental conditions. However, dynamic profiling must be able to accurately capture application behavior without incurring significant runtime overheads. We present several profiling methods for dynamically monitoring sensor-based platforms and analyze the associated network traffic, energy, and code impacts.

Original language	English (US)
Article number	5430950
Pages (from-to)	10-13
Number of pages	4
Journal	IEEE Embedded Systems Letters
Volume	2
Issue number	1
DOIs	https://doi.org/10.1109/LES.2010.2045634
State	Published - Mar 2010
Externally published	Yes

Keywords

Application-specific optimization
Design automation
Dynamic profiling
Sensor networks

ASJC Scopus subject areas

Control and Systems Engineering
General Computer Science

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10.1109/LES.2010.2045634

Cite this

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abstract = "To reduce the complexity associated with application-specific tuning of sensor-based systems, dynamic profiling enables an accurate view of the application behavior, such that the network can be reoptimized at runtime in response to changing application behavior or environmental conditions. However, dynamic profiling must be able to accurately capture application behavior without incurring significant runtime overheads. We present several profiling methods for dynamically monitoring sensor-based platforms and analyze the associated network traffic, energy, and code impacts.",

keywords = "Application-specific optimization, Design automation, Dynamic profiling, Sensor networks",

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note = "Funding Information: Manuscript received January 22, 2010; revised March 06, 2010: accepted March 06, 2010. Date of publication March 15, 2010. Date of current version April 26, 2010. This work was supported in part by the National Science Foundation, under Grants CNS-0834102 and CNS-0834080.",

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AU - Hiner, Jeff

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AU - Lysecky, Roman

AU - Gordon-Ross, Ann

N1 - Funding Information: Manuscript received January 22, 2010; revised March 06, 2010: accepted March 06, 2010. Date of publication March 15, 2010. Date of current version April 26, 2010. This work was supported in part by the National Science Foundation, under Grants CNS-0834102 and CNS-0834080.

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AB - To reduce the complexity associated with application-specific tuning of sensor-based systems, dynamic profiling enables an accurate view of the application behavior, such that the network can be reoptimized at runtime in response to changing application behavior or environmental conditions. However, dynamic profiling must be able to accurately capture application behavior without incurring significant runtime overheads. We present several profiling methods for dynamically monitoring sensor-based platforms and analyze the associated network traffic, energy, and code impacts.

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Evaluation of dynamic profiling methodologies for optimization of sensor networks

Abstract

Keywords

ASJC Scopus subject areas

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