Edge Computing in Mobile Networks: Enhancing Performance and Addressing Challenges

Anwer Hasan

doi:10.47134/jtsi.v2i3.4795

Authors

Anwer Jabbar Hasan University of Misan

DOI:

https://doi.org/10.47134/jtsi.v2i3.4795

Keywords:

Edge Computing, Latency Optimization, Energy Efficiency, AI-Based Task Management, Mobile Network Security

Abstract

This research proposes a unified AI-based framework to enhance mobile network performance using edge computing. It introduces ARMA for latency reduction and CETO for energy optimization. Both algorithms rely on predictive analytics and adaptive task management. Implemented in Python and validated using NS-3 simulations and real telecom data, ARMA reduced latency by up to 50%, while CETO decreased energy use by 35%. Results were statistically significant (p < 0.05) across urban and rural scenarios. The framework provides a scalable, efficient, and secure solution for edge deployment, supporting real-time applications such as IoT and autonomous systems.

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