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Mobile Autonomous Sensing Unit (MASU): A Framework That Supports Distributed Pervasive Data Sensing.

Esunly Medina1, David Lopez2, Roc Meseguer3

  • 1Department of Computer Architecture, Universitat Politècnica de Catalunya, Barcelona 08034, Spain. esunlyma@ac.upc.edu.

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|July 14, 2016
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Summary
This summary is machine-generated.

This study introduces a new framework for distributed pervasive data sensing. The framework enhances the reusability and generalization of sensing applications in various domains, especially for distributed work.

Keywords:
distributed pervasive data sensingmobile collaborationpervasive monitoringsoftware development framework

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Area of Science:

  • Computer Science
  • Ubiquitous Computing
  • Distributed Systems

Background:

  • Pervasive data sensing identifies behaviors and patterns unobtrusively across domains.
  • Existing sensing applications are often domain-specific, limiting reuse.
  • Current platforms restrict device and environment compatibility, hindering distributed sensing.

Purpose of the Study:

  • To present a generic framework supporting distributed pervasive data sensing.
  • To enable developers to implement sensing applications with reduced complexity and effort.
  • To facilitate sensing in loosely-coupled or fully distributed work environments.

Main Methods:

  • Development of a novel framework for distributed pervasive data sensing.
  • Evaluation through computer simulations.
  • Empirical testing in a real-world scenario.

Main Results:

  • The framework demonstrated effectiveness in supporting distributed sensing.
  • The proposed solution proved useful in loosely-coupled and fully distributed work.
  • Reduced complexity and effort for developers implementing sensing applications.

Conclusions:

  • The developed framework offers a generic and reusable approach to pervasive data sensing.
  • It effectively addresses the limitations of existing platforms for distributed environments.
  • The framework is a valuable tool for enabling pervasive sensing in diverse, distributed work settings.