An Efficient Bit Vector Approach to Semantics-Based Machine Perception in Resource-Constrained Devices

  • Cory Henson
  • Krishnaprasad Thirunarayan
  • Amit Sheth
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7649)


The primary challenge of machine perception is to define efficient computational methods to derive high-level knowledge from low-level sensor observation data. Emerging solutions are using ontologies for expressive representation of concepts in the domain of sensing and perception, which enable advanced integration and interpretation of heterogeneous sensor data. The computational complexity of OWL, however, seriously limits its applicability and use within resource-constrained environments, such as mobile devices. To overcome this issue, we employ OWL to formally define the inference tasks needed for machine perception – explanation and discrimination – and then provide efficient algorithms for these tasks, using bit-vector encodings and operations. The applicability of our approach to machine perception is evaluated on a smart-phone mobile device, demonstrating dramatic improvements in both efficiency and scale.


Machine Perception Semantic Sensor Web Sensor Data Mobile Device Resource-Constrained Environments 


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Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Cory Henson
    • 1
  • Krishnaprasad Thirunarayan
    • 1
  • Amit Sheth
    • 1
  1. 1.Ohio Center of Excellence in Knowledge-Enabled Computing (Kno.e.sis)Wright State UniversityDaytonUSA

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