AI-DRIVEN EDGE COMPUTING IN 6G NETWORKS

Authors

  • Kaveri Assistant Professor, Department of Master of Computer Application, Guru Nanak Dev Engineering College, Bidar, India
  • Ambika B Assistant Professor, Department of Master of Computer Application, Guru Nanak Dev Engineering College, Bidar, India
  • Sidappa 2nd Semester, Department of Master of Computer Applications, Guru Nanak Dev Engineering College, Bidar, India
  • Roshan 2nd Semester, Department of Master of Computer Applications, Guru Nanak Dev Engineering College, Bidar, India

Keywords:

6G Networks, Edge Computing, Artificial Intelligence, Edge Intelligence, Machine Learning, Deep Learning, Ultra-Low Latency, Internet of Things (IoT), Federated Learning, Network Optimization, Smart Systems, Autonomous Networks

Abstract

The emergence of sixth-generation (6G) wireless networks is expected to support highly dynamic, data-intensive, and latency-sensitive applications such as autonomous systems, immersive communication, and large-scale Internet of Things environments. To meet these demands, the integration of artificial intelligence with edge computing has become a critical research direction. This paper investigates the role of AI-driven edge computing in enabling efficient and intelligent 6G networks. The primary objective of this study is to analyze how embedding intelligence at the network edge can enhance real-time data processing, reduce latency, and optimize resource utilization.

A layered architecture is considered in which data generated by distributed devices is processed locally using lightweight AI models deployed at edge nodes, while complex analytics are handled by centralized cloud systems. The study evaluates the impact of this approach on system performance, particularly in terms of response time, bandwidth efficiency, and scalability. The findings indicate that AI-driven edge computing significantly improves network responsiveness and reduces dependency on centralized infrastructure. Additionally, it enables adaptive decision-making and supports emerging applications requiring ultra-reliable and low-latency communication.

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Additional Files

Published

01-06-2026

How to Cite

Kaveri, Ambika B, Sidappa, & Roshan. (2026). AI-DRIVEN EDGE COMPUTING IN 6G NETWORKS. International Educational Journal of Science and Engineering, 9(05), 650–657. Retrieved from https://iejse.com/journals/index.php/iejse/article/view/406

Issue

Vol. 9 No. 05 (2026): SPECIAL ISSUE MAY - NATIONAL CONFERENCE ON EMERGING TRENDS IN ENGINEERING, TECHNOLOGY AND MANAGEMENT

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Articles

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