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

**Authors:** Alaa Kamal Yousif Dafhalla, Tahani Abdalla Attia Gasmalla, Ameni Filali, Nada Mohamed Osman Sid Ahmed, Tijjani Adam, Mohamed Elshaikh Elobaid, Subash Chandra Bose Gopinath

PMC · DOI: 10.1016/j.isci.2026.114626 · iScience · 2026-01-03

## TL;DR

This paper reviews how AI and modern communication technologies can enhance nanosensor networks for healthcare, environmental monitoring, and smart infrastructure.

## Contribution

It introduces a unified framework for intelligent, resource-efficient nanosensor communication systems using AI and novel architectures.

## Key findings

- Machine learning improves data routing, anomaly detection, and predictive maintenance in nanosensor networks.
- Edge computing and cloud federated models enhance system performance in terms of latency and energy efficiency.
- Biological-inspired solutions and quantum-based learning offer potential for addressing computational and privacy challenges.

## Abstract

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.

Applied sciences; Engineering; Sensor system

## Full text

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## Figures

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## References

212 references — full list in the complete paper: https://tomesphere.com/paper/PMC12861007/full.md

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Source: https://tomesphere.com/paper/PMC12861007