Efficient AI Architectures and Advanced Network Technologies for Future Intelligent Systems: The ICANOS Framework

Transformer architectures have redefined the state of the art across natural language processing, computer vision, and signal processing, yet their quadratic computational complexity with respect to sequence length creates serious scalability and sustainability bottlenecks. At the same time, the convergence of sixth-generation (6G) wireless communication, the Internet of Things (IoT), and AI-native network intelligence introduces new complexity in resource management, proactive security, and energy governance. This paper argues that optimizing AI models or network infrastructure in isolation is insufficient: a tightly co-designed framework is needed that treats both concerns as inseparable. We survey efficient Transformer variants and hardware-aware approaches, and we characterize the networking landscape through 6G ubiquitous connectivity, AI-driven network automation, edge computing for IoT, and flexible network architectures. Drawing on these two bodies of literature, we propose the Intelligent Converged AI-Network Orchestration and Security (ICANOS) Framework, a three-layer AI-driven architecture comprising a Holistic Resource Orchestrator (HRO), a Proactive Security Manager (PSM), and an Energy Efficiency Optimizer (EEO). Unlike conventional software-defined networking controllers and network-functions-virtualization orchestrators, which treat AI models as opaque, schedulable workloads, ICANOS treats AI model behavior itself as a first-class signal for resource allocation and threat detection. Simulated benchmarks demonstrate energy savings of 57.9%, latency reduction of 63%, and a threat-detection improvement of 32.7% relative to non-integrated baselines. All experimental components use open-source, locally deployable models and publicly available datasets, and fully instrumented code with computational cost logging is provided for reproducibility.