Model-Driven Optimization of Data-Adaptable Embedded Systems

Adrian Lizarraga, Roman Lysecky, Jonathan Sprinkle

Research output: Chapter in Book/Report/Conference proceedingConference contribution

1 Scopus citations


Complex sensing and decision applications such as object tracking and classification, video surveillance, unmanned aerial vehicle flight decisions, and others operate on vast data streams with dynamic characteristics. As the availability and quality of the sensed data changes, the underlying models and decision algorithms should continually adapt in order to meet desired high-level requirements. Due to the complexity of such dynamic data-driven systems, traditional design time techniques are often incapable of producing a solution that remains optimal in the face of dynamically changing data, algorithms, and even availability of computational resources. To assist developers of these systems, we present a modeling and optimization methodology that enables developers to capture application task flows and data sources, define associated quality metrics with data types, specify each algorithm's data and quality requirements, and define a data quality estimation framework to optimize the application at runtime. We demonstrate each facet of the modeling and optimization process via a video-based vehicle tracking and collision avoidance application, and show how such an approach results in efficient design space exploration when selecting the optimal set of algorithm modalities. When searching for an application configuration within 1% to 5% of optimal, our model-guided approach can achieve speedups of up to 9.3X versus a standard genetic algorithm and speedups of up to 80X relative to a brute force algorithm.

Original languageEnglish (US)
Title of host publicationProceedings - 2016 IEEE 40th Annual Computer Software and Applications Conference, COMPSAC 2016
EditorsWilliam Claycomb, Dejan Milojicic, Ling Liu, Mihhail Matskin, Zhiyong Zhang, Sorel Reisman, Hiroyuki Sato, Zhiyong Zhang, Sheikh Iqbal Ahamed
PublisherIEEE Computer Society
Number of pages10
ISBN (Electronic)9781467388450
StatePublished - Aug 24 2016
Event2016 IEEE 40th Annual Computer Software and Applications Conference, COMPSAC 2016 - Atlanta, United States
Duration: Jun 10 2016Jun 14 2016

Publication series

NameProceedings - International Computer Software and Applications Conference
ISSN (Print)0730-3157


Other2016 IEEE 40th Annual Computer Software and Applications Conference, COMPSAC 2016
Country/TerritoryUnited States


  • Software modeling
  • design space exploration
  • dynamic data-driven systems
  • dynamic optimization

ASJC Scopus subject areas

  • Software
  • Computer Science Applications


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