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AI edge cloud service provisioning for knowledge management smart applications.

Antonio Maciá-Lillo1, Higinio Mora2, Antonio Jimeno-Morenilla2

  • 1Department of Computer Science and Technology (DTIC), University of Alicante, 03690, San Vicente del Raspeig, Spain. a.macia@ua.es.

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This study proposes a serverless edge-cloud architecture for smart city knowledge management, enhancing efficiency and reducing latency. The hybrid approach optimizes data processing and computation for Society 5.0 goals.

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Area of Science:

  • Computer Science
  • Urban Planning
  • Information Systems

Background:

  • Smart cities aim for human-centered, data-driven environments aligned with Society 5.0.
  • Knowledge Management (KM) processes are crucial for smart city functionality.
  • Existing architectures face challenges in latency, cost, and scalability for KM.

Purpose of the Study:

  • To investigate a novel serverless edge-cloud architecture for smart city KM processes.
  • To enhance the responsiveness and efficiency of smart city applications.
  • To analyze the characteristics, opportunities, and limitations of hybrid edge-cloud environments for KM.

Main Methods:

  • Proposed a hybrid architecture combining cloud, edge, and serverless computing.
  • Leveraged cloud for scalability and edge for proximity-based processing.
  • Conducted experimental analysis to evaluate performance benefits.

Main Results:

  • Demonstrated reduced data transmission times and alleviated network congestion.
  • Showcased cost-efficient computation options within the hybrid architecture.
  • Identified key characteristics, opportunities, and limitations of edge and cloud environments for KM.

Conclusions:

  • The serverless edge-cloud architecture effectively supports KM processes in smart cities.
  • This framework offers flexibility for advancing knowledge-driven services and smart city applications.
  • Optimized data handling and reduced latency are key benefits for urban governance.