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

Area of Science:

• Nanosensor Networks
• Information and Communication Technologies
• Artificial Intelligence
• Machine Learning

Background:

• Nanosensor networks are increasingly integrated with modern communication technologies.
• Critical needs in healthcare, environmental monitoring, and smart infrastructure require advanced solutions.
• Machine learning (ML) and artificial intelligence (AI) offer potential improvements.

Purpose of the Study:

• To examine the integration of nanosensor networks with information and communication technologies.
• To evaluate the role of ML and AI in enhancing nanosensor network performance.
• To explore new system architectures and address key challenges.

Main Methods:

• Review and analysis of existing literature on nanosensor networks and AI/ML.
• Comparison of supervised, unsupervised, reinforcement, and deep learning methods.
• Assessment of system architectures like edge computing and federated learning models.

Main Results:

• ML and AI techniques significantly improve data processing, energy management, and real-time communication in nanosensor networks.
• Various learning approaches demonstrate effectiveness in data routing, anomaly detection, security, and predictive maintenance.
• New architectures enhance performance indicators like latency, throughput, and energy efficiency.

Conclusions:

• Intelligent and resource-efficient nanosensor communication systems are achievable through AI/ML integration.
• Addressing challenges like computational load, data privacy, and interoperability is crucial.
• Future solutions may involve bio-inspired systems, interpretable models, and quantum learning.