Illiad: a Resource-Efficient Audio Sensing Service Based on the Guided Search Model
Long Le, David Jun, Douglas L. Jones

Citation
Long Le, David Jun, Douglas L. Jones. "Illiad: a Resource-Efficient Audio Sensing Service Based on the Guided Search Model". Talk or presentation, November, 2014; Poster presented at the 2014 TerraSwarm Annual Meeting.

Abstract
To handle various applications' queries, sensing services in the Internet of Things (IoT) must often process a large volume of unstructured data, i.e. audio/video data, generated across both time and space by autonomous sensors. As the IoT expands at an exponential rate, the amount of unstructured data that need processing has become unprecedentedly large, rendering the naive solution that streams all these data to the cloud either infeasible or cost-prohibitive. Enabling sensing services using finite sensing, computing, and storage resources requires a new paradigm. Inspired by the Visual Guided Search model of the human visual detection and search system, we introduce Illiad, a resource-efficient audio sensing service that is based on the Guided Search paradigm. The paradigm is structured around two levels, with a key idea that information in the first, application-agnostic level can be used to guide the deployment of resources for application-specific processing in the second level. This way resources are deployed exactly where they are most useful, and the system remains resource-efficient across all levels. The Illiad service was used to create several web apps that, to the authors' best knowledge, have not been done before. Evaluation shows that, on average, the Illiad audio sensing service achieves a saving ratio of 12 X in term of bandwidth and storage, compared to the naive approach of sending all data to the cloud. In addition, its Android sensing platform only increases the energy consumption by 10%-27% compared to continuous recording.

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Citation formats  
  • HTML
    Long Le, David Jun, Douglas L. Jones. <a
    href="http://www.terraswarm.org/pubs/429.html"><i>Illiad:
    a Resource-Efficient Audio Sensing Service Based on the
    Guided Search Model</i></a>, Talk or
    presentation,  November, 2014; Poster presented at the <a
    href="http://www.terraswarm.org/conferences/14/annual"
    >2014 TerraSwarm Annual Meeting</a>.
  • Plain text
    Long Le, David Jun, Douglas L. Jones. "Illiad: a
    Resource-Efficient Audio Sensing Service Based on the Guided
    Search Model". Talk or presentation,  November, 2014;
    Poster presented at the <a
    href="http://www.terraswarm.org/conferences/14/annual"
    >2014 TerraSwarm Annual Meeting</a>.
  • BibTeX
    @presentation{LeJunJones14_IlliadResourceEfficientAudioSensingServiceBasedOnGuided,
        author = {Long Le and David Jun and Douglas L. Jones},
        title = {Illiad: a Resource-Efficient Audio Sensing Service
                  Based on the Guided Search Model},
        month = {November},
        year = {2014},
        note = {Poster presented at the <a
                  href="http://www.terraswarm.org/conferences/14/annual"
                  >2014 TerraSwarm Annual Meeting</a>.},
        abstract = {To handle various applications' queries, sensing
                  services in the Internet of Things (IoT) must
                  often process a large volume of unstructured data,
                  i.e. audio/video data, generated across both time
                  and space by autonomous sensors. As the IoT
                  expands at an exponential rate, the amount of
                  unstructured data that need processing has become
                  unprecedentedly large, rendering the naive
                  solution that streams all these data to the cloud
                  either infeasible or cost-prohibitive. Enabling
                  sensing services using finite sensing, computing,
                  and storage resources requires a new paradigm.
                  Inspired by the Visual Guided Search model of the
                  human visual detection and search system, we
                  introduce Illiad, a resource-efficient audio
                  sensing service that is based on the Guided Search
                  paradigm. The paradigm is structured around two
                  levels, with a key idea that information in the
                  first, application-agnostic level can be used to
                  guide the deployment of resources for
                  application-specific processing in the second
                  level. This way resources are deployed exactly
                  where they are most useful, and the system remains
                  resource-efficient across all levels. The Illiad
                  service was used to create several web apps that,
                  to the authors' best knowledge, have not been done
                  before. Evaluation shows that, on average, the
                  Illiad audio sensing service achieves a saving
                  ratio of 12 X in term of bandwidth and storage,
                  compared to the naive approach of sending all data
                  to the cloud. In addition, its Android sensing
                  platform only increases the energy consumption by
                  10%-27% compared to continuous recording. },
        URL = {http://terraswarm.org/pubs/429.html}
    }
    

Posted by Long Le on 4 Nov 2014.

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