基于节能层次聚类的动态数据融合算法,用于智能农业中的无线传感器网络
Dhamodharan Srinivasan1, Ajmeera Kiran2, S Parameswari3
1Department of Computer and Communication Engineering, Sri Eshwar College of Engineering, Coimbatore, India. dhamu20@gmail.com.
Scientific reports
|February 28, 2025
概括
智能农业利用无线传感器网络 (WSN) 进行监控. 本研究介绍了层次聚类和动态数据融合,以提高WSN的能源效率和智能农业中的事件检测精度.
科学领域:
- 农业技术 农业技术
- 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:
- 智能农业依赖于无线传感器网络 (WSN) 进行环境监测.
- WSN 数据可能会有噪音,冗余和能源限制,妨碍有效的农场管理.
- 精确的事件检测和资源优化是智能农业的关键挑战.
研究的目的:
- 提出一种新的方法来提高智能农业的WSN中的能源效率和事件检测精度.
- 解决数据冗余问题,提高农场状态监测的可靠性.
- 开发基于WSN等级聚类的动态数据融合技术.
主要方法:
- 层次聚类用于将WSN节点分组为群集.
- 集群内部使用动态数据融合来聚合和合成传感器数据.
- 极端学习机器 (ELM) 用于实时事件分类和预测.
主要成果:
- 拟议的方法显著提高了WSNs的能源效率.
- 事件检测精度大大提高,可以实时识别关键的农业事件.
- 该系统的准确度约为99.54%,比现有方法高出1.81%.
结论:
- 基于层次聚类的动态数据融合为WSN在智能农业中的挑战提供了强大的解决方案.
- 集成ELM进一步提高了系统的预测和分类能力.
- 这种方法代表了通过先进的数据处理优化智能农业实践的宝贵贡献.
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