在纳米传感器网络系统中用于智能ICT的AI驱动路由和分层架构
Alaa Kamal Yousif Dafhalla1, Tahani Abdalla Attia Gasmalla1, Ameni Filali1
1Department of Computer Engineering, College of Computer Science and Engineering, University of Ha'il, Hail, Saudi Arabia.
iScience
|February 3, 2026
概括
本综述探讨了纳米传感器网络与人工智能和机器学习的整合,以改善医疗保健,环境监测和智能基础设施. 这些智能系统增强了数据处理,通信和能源效率,从而产生广泛的社会影响.
科学领域:
- 纳米传感器网络
- 信息和通信技术信息和通信技术
- 人工智能的人工智能
- 机器学习 机器学习
背景情况:
- 纳米传感器网络越来越多地与现代通信技术相集成.
- 医疗保健,环境监测和智能基础设施方面的关键需求需要先进的解决方案.
- 机器学习 (ML) 和人工智能 (AI) 提供了潜在的改进.
研究的目的:
- 检查纳米传感器网络与信息和通信技术的整合.
- 评估ML和AI在提高纳米传感器网络性能方面的作用.
- 探索新的系统架构并应对关键挑战.
主要方法:
- 审查和分析关于纳米传感器网络和AI/ML的现有文献.
- 监督,无监督,强化学习和深度学习方法的比较.
- 评估系统架构,如边缘计算和联合学习模型.
主要成果:
- 机器学习和人工智能技术显著改善了纳米传感器网络中的数据处理,能源管理和实时通信.
- 各种学习方法在数据路由,异常检测,安全和预测性维护方面表现出有效性.
- 新架构增强性能指标,如延迟,吞吐量和能源效率.
结论:
- 智能和资源高效的纳米传感器通信系统可以通过AI/ML集成来实现.
- 解决诸如计算负载,数据隐私和互操作性等挑战至关重要.
- 未来的解决方案可能包括生物灵感系统,可解释模型和量子学习.
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