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Collaborative Working Architecture for IoT-Based Applications.

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This summary is machine-generated.

New sensing applications require advanced computing. This study introduces a collaborative Internet of Things (IoT) and Mobile Cloud Computing (MCC) architecture to efficiently process complex data, enhancing IoT capabilities.

Keywords:
computer modellingembedded systemsinternet of thingsmobile cloud computingsensor processing modeling

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Area of Science:

  • Computer Science
  • Electrical Engineering
  • Information Technology

Background:

  • Modern sensing applications generate vast amounts of data, demanding significant computational power.
  • The Internet of Things (IoT) integrates processing and communication into devices, while Cloud Computing offers external resources.
  • Integrating IoT and Cloud Computing presents challenges in system interoperability and resource management.

Purpose of the Study:

  • To propose a collaborative model and architecture for enhanced computing in sensing applications.
  • To leverage Mobile Cloud Computing (MCC) for efficient data processing within an IoT framework.
  • To address the research gap in seamlessly integrating diverse IoT and cloud systems.

Main Methods:

  • Developed a novel, multi-level network architecture combining sensing and processing capabilities.
  • Integrated the Mobile Cloud Computing (MCC) paradigm into the proposed IoT architecture.
  • Designed a collaborative model to optimize the use of available computing resources.

Main Results:

  • The proposed architecture demonstrates flexibility in handling complex computational tasks.
  • An experimental validation confirmed the approach's applicability in diverse real-world scenarios.
  • The system effectively combines sensing and processing, addressing the needs of advanced IoT applications.

Conclusions:

  • The developed architecture provides a viable solution for the computational demands of advanced sensing applications.
  • The integration of IoT, Cloud Computing, and MCC offers a flexible and powerful framework.
  • Further research can build upon this model to optimize resource allocation and system performance.