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.
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 1/10of the traditional methods and does not exceed the traditional miss-hit rate.
出处
《西安交通大学学报》
EI
CAS
CSCD
北大核心
2010年第10期30-35,共6页
Journal of Xi'an Jiaotong University
基金
国家自然科学基金资助项目(60872009
60602016)
国家"863计划"资助项目(2007AA01Z428
2009AA01Z148)
安徽高校省级自然科学研究计划重大项目(ZD2008005-2
ZD200904
JK2009A013
JK2009A025)
关键词
传感器数据
事件
错误
系统化区分框架
sensor data event error systematic distinction framework
