EDGE AI-DRIVEN INTELLIGENT IOT ARCHITECTURE FOR REAL-TIME SMART CITY APPLICATIONS

Authors

  • Dr. Nguyen Minh Duc

    School of Information Technology Vietnam Institute of Digital Innovation, Hanoi, Vietnam

    Author

  • Dr. Tran Thi Linh

    Department of Artificial Intelligence Ho Chi Minh Advanced Technology University, Ho Chi Minh City, Vietnam

    Author

Keywords:
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Abstract

The rapid expansion of Internet of Things (IoT) technologies has transformed conventional urban environments into interconnected smart city ecosystems capable of delivering intelligent services across transportation, healthcare, environmental monitoring, public safety, energy management, and governance. However, the exponential growth of IoT devices has generated unprecedented volumes of heterogeneous data that challenge traditional cloud-centric computing architectures due to increased communication latency, bandwidth consumption, privacy concerns, and limited responsiveness for mission-critical applications. Edge Artificial Intelligence (Edge AI) has emerged as an effective paradigm that integrates artificial intelligence capabilities directly at edge devices and edge servers, enabling real-time data processing, intelligent decision-making, and decentralized service delivery while minimizing dependence on centralized cloud infrastructures. This review investigates the evolution of Edge AI-driven IoT architectures designed for real-time smart city applications by synthesizing recent research contributions related to intelligent edge computing, distributed AI inference, cybersecurity, workflow automation, digital twins, cloud-edge collaboration, predictive analytics, and resilient system engineering. The paper proposes a comprehensive multi-layer intelligent architecture integrating sensing devices, edge intelligence, communication networks, cloud orchestration, and application services for scalable smart city deployment. Furthermore, it critically evaluates architectural components, AI-driven resource optimization mechanisms, security strategies, performance considerations, and emerging challenges influencing future urban digital ecosystems. The analysis demonstrates that combining Edge AI with intelligent IoT infrastructures significantly improves latency, scalability, energy efficiency, operational reliability, privacy preservation, and autonomous decision-making compared with traditional centralized architectures. The proposed framework contributes a unified conceptual foundation for researchers and practitioners seeking to develop secure, adaptive, and intelligent smart city infrastructures capable of supporting next-generation urban services.

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Published
2026-07-20
Issue
Vol. 14 No. 07 (2026): Volume 14 Issue 07
Section
Articles
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Copyright (c) 2026 Dr. Nguyen Minh Duc, Dr. Tran Thi Linh (Author)

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How to Cite

Dr. Nguyen Minh Duc, & Dr. Tran Thi Linh. (2026). EDGE AI-DRIVEN INTELLIGENT IOT ARCHITECTURE FOR REAL-TIME SMART CITY APPLICATIONS. IFSMRC African International Journal of Research in Management, 14(07), 01-25. https://ifsmrc.org/index.php/aijrm/article/view/477

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