为了实现智能城市应用的AI驱动的数据减少框架
Laercio Pioli1, Douglas D J de Macedo1,2, Daniel G Costa3
1INE, Computer Science Department, Federal University of Santa Catarina, Florianopolis 88040-370, Brazil.
Sensors (Basel, Switzerland)
|January 23, 2024
概括
物联网 (IoT) 数据减少对于智能城市至关重要. 这项研究提出了一个使用机器学习模型选择最佳数据减少技术的AI框架,Huffman算法对时间序列数据显示出卓越的性能.
科学领域:
- 计算机科学 计算机科学
- 数据科学数据科学数据科学
- 人工智能的人工智能
背景情况:
- 物联网 (IoT) 产生了大量的异质数据,这给存储,带宽和计算带来了挑战.
- 拥有多个数据源的智能城市环境加剧了这些数据管理问题.
- 人工智能驱动的数据减少技术为缓解这些挑战提供了有希望的解决方案.
研究的目的:
- 为物联网应用中异质数据减少提出一个新的框架.
- 开发一个机器学习模型来预测扭曲和减少率,以指导技术选择.
- 将数据生产者背景纳入适当的减少算法的选择中.
主要方法:
- 开发一种机器学习模型,以预测数据扭曲和减少率.
- 整合数据生产者环境,以限制算法选择.
- 评估各种数据缩小技术,包括哈夫曼算法.
主要成果:
- 拟议的框架有效地减少了异构的数据量.
- 机器学习模型准确地预测了扭曲率和减少率.
- 哈夫曼算法在时间序列数据减少方面表现出卓越的性能.
结论:
- 开发的AI框架为物联网系统中的数据减少提供了有效的方法.
- 哈夫曼算法是一种高效的方法,用于减少智能城市环境中的时间序列数据.
- 这项研究为优化智能城市物联网部署中的资源利用提供了重大潜力.
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