AI-Driven Network Management for IoT Ecosystems

Abstract

The explosive proliferation of Internet of Things (IoT) devices has created increasingly complex, heterogeneous network environments, posing significant challenges for efficient and secure network management. Traditional, centralized approaches struggle with scalability, dynamic topologies, and latency constraints. Artificial Intelligence (AI), particularly machine learning and distributed paradigm architectures such as federated learning, emerges as a powerful enabler to address these issues. AI-driven network management systems employ intelligent algorithms for anomaly detection, dynamic resource allocation, traffic optimization, and predictable maintenance. They facilitate autonomous decision-making, adaptive optimization, and real-time insights—enabling networks to selfmonitor, self-heal, and self-optimize. Approaches such as pervasive AI and AI-native frameworks (e.g., vertical heterogeneous networks or VHetNets) advance these capabilities, especially when paired with edge or distributed computing paradigms. AI techniques enhance reliability, reduce downtime, improve QoS, and strengthen security postures through proactive threat identification. Despite the benefits, challenges persist: ensuring data quality, interoperability, initial deployment costs, explainability, governance, and skilled personnel. This paper presents a holistic overview of AI-driven IoT network management, including architecture, methodology, use-case evidence, workflows, advantages and limitations, followed by future directions.