Accuracy-guided runtime adaptive profiling optimization of wireless sensor networks

Lu Ding, Adrian Lizarraga, Susan Lysecky, Roman Lysecky, Ann Gordon-Ross

Research output: Contribution to conferencePaperpeer-review

Abstract

Optimization of sensor networks relies on accurate profiling information collected about the state of individual nodes and the network as a whole. A single fixed profiling methodology may incur significant overheads on the sensor network or produce inaccurate profiling results due to dynamic changes in application behavior at runtime. Alternatively, reconfiguring the profiling methodology at runtime in response to such changes can help maintain the accuracy of profiling results while minimizing the associated overheads. In this paper, we present a runtime adaptive profiling methodology that can adapt to runtime behavior of the network and preserve the accuracy of profiling data. This runtime adaptive profiling strategy further allows application experts to control the profiling accuracy, thereby providing a mechanism to tradeoff accuracy and overhead. Experimental results demonstrate that network, computational time, and power consumption overheads can be reduced by more than 50% compared to using a fixed profiling methodology while only missing 2% of profiled events.

Original languageEnglish (US)
Pages82-91
Number of pages10
DOIs
StatePublished - Oct 15 2013
Event20th Annual IEEE International Conference and Workshops on the Engineering of Computer Based Systems, ECBS 2013 - Phoenix, AZ, United States
Duration: Apr 22 2013Apr 24 2013

Other

Other20th Annual IEEE International Conference and Workshops on the Engineering of Computer Based Systems, ECBS 2013
Country/TerritoryUnited States
CityPhoenix, AZ
Period4/22/134/24/13

Keywords

  • distributed embedded systems
  • dynamic optimization
  • runtime adaptive profiling
  • sensor networks

ASJC Scopus subject areas

  • Computer Science(all)
  • Control and Systems Engineering

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