区块链和物联网集成用于使用优化的神经网络进行安全的短期和长期空气质量监测系统
Balasubramanian Chinnappan1, Kareemullah Hakim2, Neelam Sanjeev Kumar3
1Electronics and Instrumentation Engineering, B.S.A Crescent Institute of Science and Technology, Vandalur, Chennai, 600048, Tamil Nadu, India.
概括
本研究介绍了一种使用神经网络和区块链的安全空气质量监测系统,用于准确的污染预测和早期检测. 综合方法确保数据完整性,并通过可靠的环境洞察力增强公共安全.
科学领域:
- 环境科学 环境科学
- 计算机科学 计算机科学
- 数据科学数据科学数据科学
背景情况:
- 准确的空气污染预测对公共健康和福祉至关重要.
- 现有的监控系统经常面临数据完整性和安全方面的挑战.
- 需要强大的,实时的空气质量监测解决方案.
研究的目的:
- 开发一个安全的空气质量监测系统,整合神经网络和区块链.
- 为了实现精确的,实时的空气污染预测和早期检测.
- 确保空气质量数据管理中的数据完整性,安全性和透明度.
主要方法:
- 使用物联网 (IoT) 传感器收集PM2.5,PM10,CO,NO2和SO2数据.
- 实施了基于图形注意力的深度残留收缩网络和双向长短期内存 (GRNBTM) 模型进行预测.
- 集成的区块链技术带有轻量级的共识算法,用于安全,防改的数据存储.
- 采用了自适应的鸟算法来进行参数优化,以减少计算复杂性.
主要成果:
- GRNBTM模型在不同时间间隔 (30分钟至1个月) 的空气质量监测中表现出卓越的性能.
- 区块链集成成功确保了空气质量数据的安全和防改存储.
- 该系统实现了准确的预测和空气污染事件的早期检测.
- 优化的神经网络参数显著降低了计算复杂性.
结论:
- 拟议的系统为实时空气质量监测和预测提供了安全可靠的解决方案.
- 将神经网络与区块链技术相结合,可以提高数据完整性和系统安全性.
- 基于准确预测的知情决策可以显著提高公共安全和福祉.
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