AI-driven routing and layered architectures for intelligent ICT in nanosensor networked systems

This review examines the emerging integration of nanosensor networks with modern information and communication technologies to address critical needs in healthcare, environmental monitoring, and smart infrastructure. It evaluates how machine learning and artificial intelligence techniques improve data processing, energy management, real-time communication, and scalable system coordination within nanosensor environments. The analysis compares major learning approaches, including supervised, unsupervised, reinforcement, and deep learning methods, and highlights their effectiveness in data routing, anomaly detection, security, and predictive maintenance. The review also assesses new system architectures based on edge computing, cloud federated models, and intelligent communication protocols, focusing on performance indicators such as latency, throughput, and energy efficiency. Key challenges involving computational load, data privacy, and system interoperability are identified, and potential solutions inspired by biological systems, interpretable models, and quantum-based learning are explored. Overall, this work provides a unified framework for advancing intelligent and resource-efficient nanosensor communication systems with broad societal impact.