在空气污染监测系统中进行数据聚合的概率集群
Vladimir Shakhov1, Olga Sokolova1
1The Artificial Intelligence Research Center, Novosibirsk State University, 630090 Novosibirsk, Russia.
Sensors (Basel, Switzerland)
|December 11, 2025
概括
本研究引入了一种基于人工智能的方法,使用预期最大化算法将空气污染传感器集成. 这种方法优化了数据传输,减少了网络负载,节省了能源,同时保持了监控准确度.
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:
- 空气污染监测系统从分布式传感器生成大量复杂的数据.
- 有效的数据聚合对于减少通信开销和确保决策数据质量至关重要.
研究的目的:
- 为空气污染监测系统中的传感器软集群提出无监督学习方法.
- 实现基于传感器集群成员的动态数据传输政策,优化网络资源利用.
主要方法:
- 利用了预期最大化 (EM) 算法,一种无监督的机器学习和概率技术.
- 根据环境数据,将传感器集成到不同的集合中,代表正常和污染区域.
- 在数据冗余性和监控准确性之间进行可调节的权衡的杆集群会员概率.
主要成果:
- 基于人工智能的集群在模拟中表现出高效率.
- 在具有适当样本大小的常见污染场景下,EM算法实现了低于5%的相对误差.
- 拟议的方法有效地区分了正常和受污染的区域,以适应数据传输.
结论:
- 开发的无监督学习方法为空气污染监测中的智能和适应性数据聚合提供了有效的方法.
- 这种技术为先进,节能,准确的环境监测系统奠定了基础.
- 该EM算法为优化传感器网络性能和数据管理提供了强大的解决方案.
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