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Adaptive sensing based on profiles for sensor systems.

Yoshiteru Ishida1, Masahiro Tokumitsu

  • 1Department of Knowledge-Based Information Engineering, Toyohashi University of Technology / 1-1, Tempaku, Toyohashi, Aichi 441-8580, Japan;

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|February 1, 2012
PubMed
Summary
This summary is machine-generated.

This research introduces a profile-based sensing framework for adaptive sensor systems. It uses models to process diverse sensor data for effective event detection and system design.

Keywords:
adaptive sensingdynamic relational networkprofilessensor systems

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Area of Science:

  • Computer Science
  • Engineering
  • Signal Processing

Background:

  • Adaptive sensor systems require robust methods for event detection.
  • Integrating heterogeneous sensor data presents significant challenges.
  • Existing frameworks may lack adaptability to diverse operational contexts.

Purpose of the Study:

  • To propose a novel profile-based sensing framework for adaptive sensor systems.
  • To demonstrate the framework's applicability across different domains.
  • To establish a structured approach for designing and building sensor systems.

Main Methods:

  • Developing models to relate heterogeneous sensor data and generated profiles.
  • Extracting key phases for sensor system development: modeling, profiling, and managing trade-offs.
  • Applying the framework to examples of automobile engine combustion control and home security systems.

Main Results:

  • The proposed framework effectively utilizes models for event detection from varied sensor inputs.
  • Three distinct phases (modeling, profiling, managing trade-offs) were identified as crucial for system development.
  • Successful application demonstrated in distinct real-world scenarios.

Conclusions:

  • The profile-based sensing framework offers a structured and adaptable approach to designing sensor systems.
  • Mapping signals to models is fundamental for achieving mission objectives in sensor system design.
  • The framework provides a scalable solution for integrating diverse data sources for event detection.