Finding outlier data is very important for sensor network. In this paper we put forward the concept of local outlier by an example. Different with traditional outlier, whether a sensor node becomes a local outlier only depends on its neighbor nodes. We give its formula definition and put forward a Naive algorithm that consume excessive energy. Then we devise a distributed algorithm named DLODA. The DLODA divides the network into many areas and selects a cluster head for each area. The cluster head can compute part results in-network and filter the data which will not become the results to save energy. Finally we evaluate the DLODA in terms of energy consumption and response time. Experiments show that the DLODA has good performance.%找到异常数据对于传感器网络来说非常重要.本文通过一个例子首先提出了局部异常的概念.与传统异常不同,局部异常仅取决于节点及其周围节点的取值.我们给出了局部异常的形式化定义,并首先提出了解决局部异常的Na(i)ve算法,由于其能耗过大,又提出了分布式的解决方案DLODA.DLODA将网络分成多个区域,每个区域选择一个簇头,簇头能够在网内提前算出部分查询结果,并过滤掉不会成为查询结果的数据,以节约能量开销.最后从能量消耗和响应时间两个方面对DLODA的性能进行了评估,实验显示DLODA具有良好性能.
展开▼
