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Anomaly detection method based on multi-dimensional Epanechnikov kernel density estimation

A technology of kernel density estimation and anomaly detection, applied in electrical components, wireless communication, network topology, etc., can solve problems such as high computational complexity and failure to meet accuracy requirements, achieve broad application prospects, reduce communication overhead, and improve accuracy Effect

Inactive Publication Date: 2014-07-09
ZHEJIANG FORESTRY UNIVERSITY
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Problems solved by technology

At present, most abnormal data detection methods for wireless sensor networks cannot meet the accuracy requirements of practical applications, and the algorithms that can meet the accuracy requirements have high computational complexity.

Method used

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  • Anomaly detection method based on multi-dimensional Epanechnikov kernel density estimation

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Embodiment 1

[0075] The distributed data flow model involved in this implementation is as follows: figure 1 shown, from figure 1 It can be seen that there are a total of l+1 distribution nodes (including a cluster head node) in the sensor network. Each distribution node in the area will collect data at a certain period to form a data stream. In order to ensure the correctness of data samples collected by distributed nodes in this area, each distributed node needs to eliminate outliers in the sliding window before uploading data.

[0076] Now take the temperature value (unit is Celsius) as an example, assuming that 10 distribution nodes (V 1 to V 10 ), the sliding window width of each distribution node is N=20, so that the data collected by 10 distribution nodes within 20 moments constitutes a 10×20 sample matrix Z:

[0077] Z=(10.18, 10.56, 11.33, 19.56, 20.6, 9.05, 10.36, 10.79, 11.59, 9.44, 10.6, 10.07, 10.73, 9.35, 10.02, 11.4, 8.2, 11.78, 11.1, 10.57;

[0078] 10.86, 9.1, 9.86, 9....

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Abstract

The invention relates to an anomaly detection method based on multi-dimensional Epanechnikov kernel density estimation. By means of the method, abnormal data can be accurately detected. According to the technical scheme, the anomaly detection method based on multi-dimensional Epanechnikov kernel density estimation sequentially comprises the steps that (1) data at all distribution nodes are collected respectively, and then abnormal value diagnosis is conducted through a sampling method based on the kth closest distance; (2) a normal data sample is formed in a cluster head node sliding window, and a kernel density estimation model is established in the cluster head node sliding window according to the sample; (3) the kernel density estimation model is sent to all the distribution nodes, and each distribution node judges whether data arriving at the distribution node at the next moment are abnormal or not through the kernel density estimation model; (4) at each time interval T, each distribution node actively sends the normal data in the latest period of time to the cluster head node; (5) the step (1) is returned to.

Description

technical field [0001] This patent relates to a method for detecting reliability of wireless sensor network data, especially a method for detecting outliers based on multidimensional Epanechnikov kernel density estimation. Background technique [0002] Many physical phenomena (such as temperature, humidity, atmospheric pressure, etc.) that exist in the real living environment need to be continuously monitored. As a very important data source, the wireless sensor network (WSN) collects data that is very susceptible to various noise sources, such as node software and hardware failures, and environmental noise encountered during node communication. These noises can seriously affect the sensor readings, as well as the distribution of the data, causing the sensor to produce inaccurate or erroneous data. Therefore, designing an effective sensor data stream analysis and processing method is the focus of research on anomaly detection in wireless sensor networks in recent years. At...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): H04W24/04H04W84/18
Inventor 李光辉朱虹
Owner ZHEJIANG FORESTRY UNIVERSITY
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