Evaluation of Dynamic Profiling Methodologies for Optimization of Sensor Networks

Shenoy, Ashish; Hiner, Jeff; Lysecky, Susan; Lysecky, Roman; Gordon‐Ross, Ann

doi:10.1109/les.2010.2045634

Cited by 9 publications

(5 citation statements)

References 9 publications

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“…A configurable profiling strategy must consider: 1) what application parameters need to be profiled, 2) when to profile 3) how to profile, 4) whom to profile, and 5) at what profile granularity [16] [3].…”

Section: Overview Of Static Profiling Methodologymentioning

confidence: 99%

“…An optimizer continuously evaluates the system against expert-defined constraints and goals, and explores a variety of system configurations. Once a suitable configuration is determined, the configuration information is sent to the physical system for platform reconfiguration [16].…”

Section: Dpop Environment and Profiler Modulementioning

confidence: 99%

“…14 15 # How to profile 16 (1) Find max transmission interval (MAX_TR) of application data packets in n profile packets 17…”

Section: A Adaptive Profiling Optimization Algorithmmentioning

confidence: 99%

“…To alleviate this problem, we previously developed a dynamic profiling and optimization (DPOP) framework [17] [16] [12] to automatically monitor the runtime state of sensor networks and optimize node parameters in order to meet application expert specified requirements. Runtime monitoring of application behavior is currently achieved by employing a configurable profiling methodology that enables application experts to specify the manner in which profile information is collected and distributed.…”

Section: Introductionmentioning

confidence: 99%

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Accuracy-Guided Runtime Adaptive Profiling Optimization of Wireless Sensor Networks

Ding

Lizarraga

Lysecky

et al. 2013

2013 20th IEEE International Conference and Workshops on Engineering of Computer Based Systems (ECBS)

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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.

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Section: Overview Of Static Profiling Methodologymentioning

confidence: 99%

Section: Dpop Environment and Profiler Modulementioning

confidence: 99%

“…14 15 # How to profile 16 (1) Find max transmission interval (MAX_TR) of application data packets in n profile packets 17…”

Section: A Adaptive Profiling Optimization Algorithmmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Accuracy-Guided Runtime Adaptive Profiling Optimization of Wireless Sensor Networks

Ding

Lizarraga

Lysecky

et al. 2013

2013 20th IEEE International Conference and Workshops on Engineering of Computer Based Systems (ECBS)

Self Cite

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show abstract

“…Dynamic profiling opens opportunity to monitor how system responds to changes in environmental conditions or changes in the underlying platform. Effectiveness of the methodology is demonstrated in reference to Forest Fire Detection and Propagation Tracking and Building Monitor applications [8]. Authors have proposed four different dynamic profiling methods suitable for applications with diverse behavior.…”

Section: Dpop Frameworkmentioning

confidence: 99%