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Related Experiment Video

Updated: Oct 7, 2025

Integration of 5G Experimentation Infrastructures into a Multi-Site NFV Ecosystem
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Recent Advances in Evolving Computing Paradigms: Cloud, Edge, and Fog Technologies.

Nancy A Angel1, Dakshanamoorthy Ravindran1, P M Durai Raj Vincent2

  • 1Department of Computer Science, St. Joseph's College (Autonomous), Bharathidasan University, Tiruchirappalli 620002, India.

Sensors (Basel, Switzerland)
|January 11, 2022
PubMed
Summary
This summary is machine-generated.

Edge computing and machine learning integration offer solutions for Internet-of-things (IoT) data challenges. This approach enhances speed, privacy, and efficiency by processing data closer to users, overcoming cloud limitations.

Keywords:
cloud computingedge computingfog computinginternet-of-thingsmachine learning

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Area of Science:

  • Computer Science
  • Electrical Engineering
  • Artificial Intelligence

Background:

  • Cloud computing is essential for Internet-of-things (IoT) but faces limitations with vast data, requiring swift responses and enhanced privacy.
  • Novel IoT applications generate massive data, straining cloud infrastructure and necessitating alternative solutions.

Purpose of the Study:

  • To survey prominent computing paradigms and their fusion with machine learning (ML).
  • To highlight innovations, open research challenges, and future prospects in edge computing for ML applications.

Main Methods:

  • Review of current computing paradigms, focusing on edge computing and its integration with ML.
  • Analysis of the benefits and challenges of deploying ML at the network edge.

Main Results:

  • Edge computing, a decentralized approach, processes data near end-users, overcoming cloud bottlenecks.
  • Deploying ML at the edge reduces latency and energy consumption while optimizing resource management, security, and privacy.

Conclusions:

  • The fusion of ML and edge computing presents a promising trend for enhancing IoT applications.
  • Further research is needed to develop ML algorithms optimized for speed, accuracy, storage, security, and low power consumption in edge environments.