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Context Aware Middleware Architectures: Survey and Challenges.

Xin Li1, Martina Eckert2, José-Fernán Martinez3

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This survey reviews context aware middleware architectures from 2009-2015. No single architecture meets all requirements, highlighting challenges for future development in context-aware computing.

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contextcontext aware middlewarecontext awarenessmodellingontologyreasoning

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Area of Science:

  • Computer Science
  • Software Engineering
  • Distributed Systems

Background:

  • Context-aware applications adapt behavior to dynamic environments.
  • Context-aware middleware simplifies development by providing generic context management.
  • Understanding context principles, modeling, and reasoning is crucial.

Purpose of the Study:

  • To survey and analyze state-of-the-art context-aware middleware architectures.
  • To provide a comprehensive overview of middleware proposed between 2009 and 2015.
  • To identify gaps and challenges in current context-aware middleware solutions.

Main Methods:

  • A systematic survey of eleven selected context-aware middleware architectures.
  • Comparative analysis based on technical parameters like architectural style, scalability, and security.
  • Evaluation of context abstraction, reasoning, and cloud integration capabilities.

Main Results:

  • Eleven context-aware middleware architectures are presented and analyzed.
  • Detailed comparisons reveal varying strengths and weaknesses across parameters.
  • No single architecture fully satisfies all identified requirements.

Conclusions:

  • The current landscape of context-aware middleware lacks a universally compliant solution.
  • Significant challenges remain in achieving comprehensive scalability, security, and interoperability.
  • Future research should address these open issues for improved context-aware system development.