Systematic Distinction of Events and Errors in Sensor Data

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Systematic Distinction of Events and Errors in Sensor Data

更新时间：2025-07-09

- Systematic Distinction of Events and Errors in Sensor Data
- Vol. 44, Issue 10, Pages: 30-35(2010)
- 作者机构：
  
  1. 中国科学技术大学计算机科学与技术学院,合肥,230027
  2. 北京邮电大学网络与交换技术国家重点实验室,北京,100876
  3. 安徽省计算与通讯软件重点实验室,合肥,230027
  4. 中科大-香港城大联合研究中心互联网服务实验室,江苏,苏州,215123
  5. 香港城市大学电脑科学系,香港
- 作者简介：
- 基金信息：
- DOI：
  CLC： TP393
- Online First：10 October 2010，
  
  Published：2010
- 稿件说明：
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Systematic Distinction of Events and Errors in Sensor Data[J]. 2010, 44(10): 30-35.
DOI：

Systematic Distinction of Events and Errors in Sensor Data[J]. 2010, 44(10): 30-35. DOI：

摘要

针对传感器网络中对事件/异常检测的研究在一定程度上忽略了区分数据样本的重要性问题

依据传感器数据的不确定性分析了事件样本和错误样本的相似点和不同点

设计了系统化区分框架

通过节点级时域处理、邻居级空间处理、聚簇级权重排序和网络级决策融合的方法逐层过滤

将原始样本集划分为正常样本集、错误样本集和事件样本集.真实数据集的实验结果显示

所提框架在不同网络质量下对样本的辨识率均在97%以上

可将误报率降低到传统事件/异常检测方法的1/10

且漏报率不超过传统方法.

Abstract

Due to neglecting the importance of distinguishing sensor data in event/anomaly detection

similarities and differences among event samples and error samples are analyzed based on the sensor data uncertainty

and a systematic distinction framework is designed to partition the raw data set into event subset

error subset and ordinary subset through node-level temporal processing

neighbor-level spatial processing

cluster-level ranking and network-level decision fusion. Experimental results on real-sensed data show that the framework achieves a distinction ratio as high as 97% in different network cases. Comparisons with traditional methods show that the proposed framework reduces the false-alarm rate to 1/10 of the traditional methods and does not exceed the traditional miss-hit rate.

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references

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