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Osmotic Cloud-Edge Intelligence for IoT-Based Cyber-Physical Systems.

Giuseppe Loseto1, Floriano Scioscia2, Michele Ruta2

  • 1Department of Management, Finance and Technology, LUM University "Giuseppe Degennaro", Strada Statale 100 km 18, I-70010 Casamassima, Italy.

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|March 26, 2022
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Summary
This summary is machine-generated.

This study introduces a Cloud-Edge AI microservice architecture for Cyber-Physical Systems. It enables flexible, opportunistic training and inference across edge and cloud resources, enhancing AI model accuracy.

Keywords:
Cloud-Edge IntelligenceCyber-Physical SystemsEdge AIInternet of ThingsOsmotic Computingmicroservice architecture

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Area of Science:

  • Cyber-Physical Systems
  • Artificial Intelligence
  • Edge Computing

Background:

  • AI in Cyber-Physical Systems leverages massive IoT data for accurate machine learning inference.
  • Edge Intelligence executes inference locally using cloud-trained models, but lacks flexible training migration.
  • Current Edge nodes struggle with dynamic training task migration between edge and cloud environments.

Purpose of the Study:

  • To propose a novel Cloud-Edge AI microservice architecture based on Osmotic Computing principles.
  • To enable flexible and dynamic training and inference capabilities across distributed edge and cloud resources.
  • To facilitate the opportunistic exploitation of computational resources for optimal AI prediction accuracy.

Main Methods:

  • Developed a containerized microservice architecture for AI training and inference.
  • Encapsulated architectural modules for direct mapping with Commercial-Off-The-Shelf (COTS) components.
  • Implemented a prototype using commodity hardware and open-source technologies.

Main Results:

  • A functional prototype demonstrating the proposed Cloud-Edge AI architecture was realized.
  • Experiments were conducted within a small-scale intelligent manufacturing case study.
  • The results validated the feasibility and significant benefits of the proposed architecture.

Conclusions:

  • The Cloud-Edge AI microservice architecture effectively supports dynamic training and inference.
  • The approach enhances AI model accuracy by opportunistically utilizing edge and cloud resources.
  • The architecture's modularity and COTS compatibility promote practical implementation in intelligent systems.