Context Aware System Design
Christine Chan, Michael Ostertag, Alper Sinan Akyurek, Tajana Simunic Rosing

Citation
Christine Chan, Michael Ostertag, Alper Sinan Akyurek, Tajana Simunic Rosing. "Context Aware System Design". SPIE Defense and Security, International Society for Optics and Photonics (SPIE), 9, April, 2017.

Abstract
The Internet of Things envisions a web-connected infrastructure of billions of sensors and actuation devices. However, the current state-of-the-art presents another reality: monolithic end-to-end applications tightly coupled to a limited set of sensors and actuators. Growing such applications with new devices or behaviors, or extending the existing infrastructure with new applications, involves redesign and redeployment. We instead propose a modular approach to these applications, breaking them into an equivalent set of functional units (context engines) whose input/output transformations are driven by general-purpose machine learning, demonstrating an improvement in compute redundancy and computational complexity with minimal impact on accuracy. In conjunction with formal data specifications, or ontologies, we can replace application-specific implementations with a composition of context engines that use common statistical learning to generate output, thus improving context reuse. We implement interconnected context-aware applications using our approach, extracting user context from sensors in both healthcare and grid applications. We compare our infrastructure to single-stage monolithic implementations with single-point communications between sensor nodes and the cloud servers, demonstrating a reduction in combined system energy by 22-45%, and multiplying the battery lifetime of power-constrained devices by at least 22x, with easy deployment across different architectures and devices.

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Citation formats  
  • HTML
    Christine Chan, Michael Ostertag, Alper Sinan Akyurek,
    Tajana Simunic Rosing. <a
    href="http://www.terraswarm.org/pubs/950.html"
    >Context Aware System Design</a>, SPIE Defense and
    Security, International Society for Optics and Photonics
    (SPIE), 9, April, 2017.
  • Plain text
    Christine Chan, Michael Ostertag, Alper Sinan Akyurek,
    Tajana Simunic Rosing. "Context Aware System
    Design". SPIE Defense and Security, International
    Society for Optics and Photonics (SPIE), 9, April, 2017.
  • BibTeX
    @inproceedings{ChanOstertagAkyurekRosing17_ContextAwareSystemDesign,
        author = {Christine Chan and Michael Ostertag and Alper
                  Sinan Akyurek and Tajana Simunic Rosing},
        title = {Context Aware System Design},
        booktitle = {SPIE Defense and Security},
        organization = {International Society for Optics and Photonics
                  (SPIE)},
        day = {9},
        month = {April},
        year = {2017},
        abstract = {The Internet of Things envisions a web-connected
                  infrastructure of billions of sensors and
                  actuation devices. However, the current
                  state-of-the-art presents another reality:
                  monolithic end-to-end applications tightly coupled
                  to a limited set of sensors and actuators. Growing
                  such applications with new devices or behaviors,
                  or extending the existing infrastructure with new
                  applications, involves redesign and redeployment.
                  We instead propose a modular approach to these
                  applications, breaking them into an equivalent set
                  of functional units (context engines) whose
                  input/output transformations are driven by
                  general-purpose machine learning, demonstrating an
                  improvement in compute redundancy and
                  computational complexity with minimal impact on
                  accuracy. In conjunction with formal data
                  specifications, or ontologies, we can replace
                  application-specific implementations with a
                  composition of context engines that use common
                  statistical learning to generate output, thus
                  improving context reuse. We implement
                  interconnected context-aware applications using
                  our approach, extracting user context from sensors
                  in both healthcare and grid applications. We
                  compare our infrastructure to single-stage
                  monolithic implementations with single-point
                  communications between sensor nodes and the cloud
                  servers, demonstrating a reduction in combined
                  system energy by 22-45%, and multiplying the
                  battery lifetime of power-constrained devices by
                  at least 22x, with easy deployment across
                  different architectures and devices. },
        URL = {http://terraswarm.org/pubs/950.html}
    }
    

Posted by Christine Chan on 17 May 2017.

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