1 J. Warren McClure School of Emerging Communication Technologies, Ohio University, Athens, Ohio, USA.
2 Department of Computer Science & Engineering, University of Fairfax, USA
3 Department of Computer Science, Maharishi International University, Fairfield, Iowa
4 Department of Computer Science, University of Texas Permian Basin, Texas, USA.
5 Department of Electrical & Electronics Engineering, Federal University of Agriculture, Abeokuta, Ogun State, Nigeria
World Journal of Advanced Engineering Technology and Sciences, 2025, 14(03), 462-495
Article DOI: 10.30574/wjaets.2025.14.3.0166
Received on 19 February 2025; revised on 29 March 2025; accepted on 31 March 2025
In recent years, the telecommunication industry has seen significant advancements with the integration of AI, cloud computing, and edge computing. These technologies, when combined, enable telecom providers to process data more effectively, minimize latency, and enhance service delivery. This paper explores the synergy between AI, cloud, and edge computing in the telecom sector, highlighting innovative approaches to real-time data processing and latency optimization. Through a deep dive into emerging trends, this article identifies novel methodologies and applications in AI-driven cloud-edge integration, with a focus on telecom infrastructure, 5G networks, and IoT ecosystems.
AI-Driven Cloud-Edge Synergy; Latency Optimization; Real-Time Data Processing; Cloud Computing; Edge Computing; Network Slicing; Machine Learning; Deep Learning; Network Function Virtualization (NFV); Software-Defined Networking (SDN); 5G Networks; 6G Networks; Autonomous Networks; Smart Cities; Traffic Management; Quality Of Service (Qos); Network Optimization.
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Adedeji Ojo Oladejo, Omoniyi David Olufemi, Eunice Kamau, David O Mike-Ewewie, Adebayo Lateef Olajide and Daniel Williams. AI-driven cloud-edge synergy in telecom: An approach for real-time data processing and latency optimization. World Journal of Advanced Engineering Technology and Sciences, 2025, 14(03), 462-495. Article DOI: https://doi.org/10.30574/wjaets.2025.14.3.0166.