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基于自适应值的自主物联网 (IoT) 数据减少
Handuo Zhang1, Jun Na2, Bin Zhang2
1School of Computer Science and Engineering, Northeastern University, Shenyang 110167, China.
Sensors (Basel, Switzerland)
|December 9, 2023
概括
本研究介绍了物联网 (IoT) 数据减少的自适应值方法. 它动态平衡数据减少率和重建精度,提高智能物联网应用程序的效率.
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
- 物联网 (IoT) 的物联网 (IoT) 的物联网.
背景情况:
- 智能物联网应用程序产生了大量的传感器数据,需要减少带宽和能源效率的数据.
- 目前的数据减少方法使用固定的值,无法适应动态物联网环境,并损害重建准确性.
- 减少率和重建精度之间的权衡是关键的,但很难用静态参数进行优化.
研究的目的:
- 提出使用自适应值的自主物联网数据减少方法.
- 为了在不断变化的物联网条件下动态平衡数据减少率和重建精度.
- 提高智能物联网系统中数据处理的效率和有效性.
主要方法:
- 实施了一个适应性门,以减少物联网数据,根据数据特征动态调整.
- 集成概念漂移检测以识别物联网系统变化并触发值调整.
- 通过添加数据趋势信息来提高准确性,增强数据重建.
主要成果:
- 拟议的自适应值方法比静态方法显著改进.
- 在相同的减少率下,平均提高了精度11.7%.
- 在相同的精度下,减少率提高了17.3%.
结论:
- 适应值方法有效地平衡了动态物联网环境中的数据减少和重建精度.
- 概念漂移检测和数据趋势整合是优化物联网数据管理的关键.
- 拟议的方法为物联网数据处理中使用卡尔曼过,LMS和PIP等传统算法提供了优质的替代方案.
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