Outlier detection for wireless sensor networks using density-based clustering approach
Outlier detection for wireless sensor networks using density-based clustering approach
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DOI：
10.1049/iet-wss.2016.0044
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发表时间：
2017-08-01
影响因子：
1.9
通讯作者：
Mahfoudhi, Adel
中科院分区：
文献类型：
--
作者：
Abid, Aymen;Kachouri, Abdennaceur;Mahfoudhi, Adel
离群点检测是数据挖掘、医学、传感器网络等领域的重要研究课题。它主要用于识别入侵、欺诈、错误、缺陷、噪声等。事实上，离群值测量是对信息质量的重要改进，因为它们不符合预期的正常行为。由于感知测量的重要性是通过无线传感器网络收集的，一种新的OD过程被称为基于密度的空间聚类的应用程序与噪声（DBSCAN）-OD已开发的基础上，算法DBSCAN，作为OD的背景。相对于经典的DBSCAN方法，两个过程已经联合起来，第一个计算参数，而第二个关注类识别时空数据库。通过这两个模块，可以将实时应用案例集中在基站中，以便将异常值与正常传感器分离。为了评估作者提出的解决方案，多样性的合成数据库已被应用，从英特尔伯克利实验室的真实的测量产生。得出的模拟结果充分表明，他们设计的方法可以有效地帮助检测离群值，准确率为99%。
Outlier detection (OD) constitutes an important issue for many research areas namely data mining, medicines, and sensor networks. It is helpful mainly in identifying intrusion, fraud, errors, defects, noise and so on. In fact, outlier measurements are essential improvements to quality of information, as they are not conforming to expected normal behaviour. Due to the importance of sensed measurements is collected via wireless sensor networks, a novel OD process dubbed density-based spatial clustering of applications with noise (DBSCAN)-OD has been developed based on the algorithm DBSCAN, as a background for OD. With respect to the classic DBSCAN approach, two processes have been jointly combined, the first of computing parameters, while the second concerns class identification in spatial temporal databases. Through both of these modules, one is able to consider real-time application cases as centralised in the base station for the purpose of separating outliers from normal sensors. For the sake of evaluating the authors proposed solution, a diversity of synthetic databases has been applied as generated from real measurements of Intel Berkeley lab. The reached simulation findings indicate well that their devised method can prove to help effectively in detecting outliers with an accuracy rate of 99%.