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Summary
This summary is machine-generated.

This study introduces a self-adaptive framework for dynamic decision-making in Internet of Things (IoT) environments. The framework enhances IoT systems by adapting behavior at runtime, improving performance in changing conditions.

Keywords:
Internet of Things (IoT)Nash equilibriumfinite-state machine (FSM)game theoryself-adaptive software

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Area of Science:

  • Computer Science
  • Software Engineering
  • Artificial Intelligence

Background:

  • Dynamic environments in the Internet of Things (IoT) require adaptable software solutions.
  • Self-adaptive software is crucial for systems operating in changing conditions, such as smart homes and greenhouses.
  • Existing IoT frameworks may lack runtime adaptability for evolving requirements.

Purpose of the Study:

  • To propose a novel self-adaptive framework for real-time decision-making in dynamic IoT environments.
  • To enhance the operational efficiency and adaptability of IoT systems.
  • To demonstrate the framework's applicability in real-world scenarios.

Main Methods:

  • Development of a self-adaptive framework incorporating a finite-state machine model.
  • Integration of a game theoretic decision-making approach for strategy extraction.
  • Implementation of a prototype for empirical evaluation of runtime performance.

Main Results:

  • The proposed framework effectively supports runtime decision-making in dynamic IoT environments.
  • Experimental evaluation validated the framework's performance and applicability.
  • A smart greenhouse use case illustrated the practical usability and benefits.

Conclusions:

  • The self-adaptive framework offers a viable solution for managing dynamic IoT environments.
  • The integration of finite-state machines and game theory enables robust runtime adaptation.
  • The framework's successful application in a smart greenhouse highlights its potential for diverse IoT deployments.