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WSN (Wireless Sensor Network) abnormity detection method and system based on artificial immunization and k-means clustering

A wireless sensor network, anomaly detection technology, applied in the field of Internet of Things, can solve the problems of low anomaly detection accuracy and high energy consumption

Inactive Publication Date: 2016-07-20
CHINA UNIV OF GEOSCIENCES (WUHAN)
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Problems solved by technology

[0004] The technical problem to be solved by the present invention is to provide an artificial immunity and K Anomaly detection method and system for wireless sensor network based on mean value clustering

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  • WSN (Wireless Sensor Network) abnormity detection method and system based on artificial immunization and k-means clustering
  • WSN (Wireless Sensor Network) abnormity detection method and system based on artificial immunization and k-means clustering
  • WSN (Wireless Sensor Network) abnormity detection method and system based on artificial immunization and k-means clustering

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Embodiment Construction

[0045] In order to make the object, technical solution and advantages of the present invention clearer, the present invention will be further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described here are only used to explain the present invention, not to limit the present invention.

[0046] like figure 1 As shown, the wireless sensor network anomaly detection method based on artificial immunity and K-means clustering in the embodiment of the present invention is characterized in that it includes the following steps:

[0047] S1. Obtain the original monitoring data collected by the wireless sensor network nodes in multiple cycles to form a time series, perform normalization processing on the time series, and use the segmentation aggregation approximation method to compress and reduce the dimensionality of the normalized sequence Get the compressed sequence, and calculate the mean...

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Abstract

The invention discloses a WSN abnormity detection method and system based on artificial immunization and k-means clustering. The method comprises that S1) original monitoring data collected by WSN nodes is obtained to form a time sequence, the time sequence is normalized, compression and dimension reduction are carried out on the normalized sequence, and the mean value and variance of each sequential segments in the compressed sequence are calculated; S2) the Euclidean distance between node data and each cluster head is calculated, and an artificial immunization algorithm is used to search an optimal initial cluster head set for K-means classification; S3) whenever new data is distributed into a corresponding cluster, iterative update is carried out on the cluster head value of the cluster till the amount of all data in the cluster does not change; and S4) the WSN determines abnormity according to the amount of data in the cluster in a K-means clustering result. According to the invention, abnormity information in monitoring data can be discovered accurately, the instantaneity and reliability of abnormity event detection of the WSN are improved, and energy and communication bandwidth of the WSN are greatly reduced.

Description

technical field [0001] The invention relates to the technical field of the Internet of Things, in particular to a wireless sensor network anomaly detection method and system based on artificial immunity and K-means clustering. Background technique [0002] Wireless Sensor Networks (WSN for short) is the mainstream form of future network development, and has become a new scientific research field in this century. There are many urgent problems that need to be solved at the two levels of basic theory and engineering technology. The wireless sensor network has low cost, low power consumption, and large-scale ad hoc network; the sensor nodes are small in size, battery-powered, and flexible in deployment; and they can adapt to harsh environments where monitoring is difficult for manpower; Monitoring capabilities for disaster prevention. In order to monitor various possible emergencies (such as landslides, air pollution, forest fires, etc.) in time, it is necessary to pay attent...

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Application Information

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IPC IPC(8): H04L12/26H04W24/02H04W24/08H04W52/02H04W84/18G06K9/62G06N3/12
CPCH04L43/02H04L43/04H04L43/08H04W24/02H04W24/08H04W52/0219H04W84/18G06N3/12G06F18/23213Y02D30/70
Inventor 陈分雄凌承昆郭星锋王典洪殷蔚明付杰胡凯唐曜曜
Owner CHINA UNIV OF GEOSCIENCES (WUHAN)
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