6G-Enabled Intelligent IoT: Emerging Architectures, AI-Native Networking, Digital Twins, and Security Challenges

Abstract

The convergence of sixth-generation (6G) wireless communication, artificial intelligence (AI), edge intelligence, and the Internet of Things (IoT) is transforming the conventional connectivity-oriented IoT paradigm into an intelligent, autonomous, and context-aware cyber-physical infrastructure. Unlike previous generations, 6G is being conceived not merely as a higher-capacity communication system but as an AI-native platform capable of jointly optimizing communication, computation, sensing, control, and intelligence. This evolution is particularly significant for IoT environments, where billions of heterogeneous devices, sensors, actuators, autonomous machines, vehicles, robots, and cyber-physical assets will generate highly dynamic and geographically distributed data. This paper presents a comprehensive analysis of 6G-enabled intelligent IoT, emphasizing emerging architectures, AI-native networking, network and system digital twins, distributed intelligence, semantic communications, reconfigurable intelligent surfaces, integrated sensing and communication, and security mechanisms. A multidimensional taxonomy is developed according to communication technologies, intelligence placement, learning paradigms, architectural layers, digital-twin functions, and security mechanisms. The paper critically examines centralized, edge, federated, distributed, and multi-agent intelligence architectures and discusses the roles of deep learning, reinforcement learning, federated learning, generative AI, and explainable AI in autonomous network optimization. Particular attention is devoted to digital twins as closed-loop environments for network monitoring, prediction, experimentation, and control. Security challenges are analyzed across device, edge, radio access, AI-model, digital-twin, orchestration, and application layers, including adversarial machine learning, model poisoning, privacy leakage, identity attacks, zero-day attacks, and supply-chain vulnerabilities. Comparative analyses of technologies, datasets, simulation platforms, and performance indicators are provided. Representative applications in smart manufacturing, intelligent transportation, healthcare, agriculture, energy, robotics, and smart cities are examined, followed by an illustrative case study of an AI-native 6G smart-factory ecosystem. Finally, unresolved challenges involving scalability, synchronization, explainability, energy efficiency, interoperability, security, standardization, and trustworthy autonomy are identified. The paper concludes with a research agenda centered on self-evolving AI-native networks, secure digital-twin ecosystems, semantic intelligence, quantum-resistant security, integrated sensing-computing-communication, and sustainable 6G-IoT infrastructures.