Distributed edge AI architecture for ultra-low latency 5G applications

Pothen, Vivek Aby (2025) Distributed edge AI architecture for ultra-low latency 5G applications. World Journal of Advanced Engineering Technology and Sciences, 15 (2). pp. 128-136. ISSN 2582-8266

The integration of edge computing with 5G networks represents a transformative approach to telecommunications architecture that addresses the stringent latency requirements of next-generation applications. This article shows architectural frameworks for edge-enabled 5G deployments, latency optimization techniques, real-time AI analytics capabilities, and key application domains. The article demonstrates that edge computing significantly reduces latency compared to cloud-centric alternatives while enhancing bandwidth efficiency and computational capabilities at the network edge. Multi-access Edge Computing frameworks provide standardized integration with 5G infrastructure, enabling local data processing and cross-platform interoperability. Advanced optimization techniques, including network slicing, computational offloading, data locality, and hardware acceleration, collectively create an environment capable of supporting ultra-low latency applications. AI analytics optimized for edge deployment enable intelligent decision-making without compromising privacy or performance, while application domains spanning autonomous vehicles, industrial IoT, immersive reality experiences, and predictive maintenance showcase the practical benefits of this architectural approach. These innovations collectively establish a foundation for mission-critical applications requiring deterministic performance and real-time processing capabilities.