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Wireless sensor network outlier data self-adaption detecting method based on entropy measurement

A wireless sensor and self-adaptive detection technology, applied in wireless communication, network topology, electrical components, etc., can solve problems such as unsuitable for processing large-scale and rapidly changing data streams, affecting detection accuracy, and high time and space complexity

Active Publication Date: 2014-04-23
GUANGDONG COMM POLYTECHNIC
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  • Abstract
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AI Technical Summary

Problems solved by technology

[0007] The existing outlier data detection methods mainly focus on the rationality of outlier detection and the accuracy of the algorithm. Due to the large time and space complexity of various algorithms, they are not suitable for processing large-scale and rapidly changing data streams.
At the same time, due to ignoring the spatial or temporal correlation between data, the detection accuracy is affected. Although measures to reduce the amount of communication and calculation have been taken, there are still problems such as large communication and calculation, and only suitable for single-dimensional reading.

Method used

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  • Wireless sensor network outlier data self-adaption detecting method based on entropy measurement
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  • Wireless sensor network outlier data self-adaption detecting method based on entropy measurement

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Experimental program
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Embodiment 1

[0108] Embodiment 1: Outlier data detection simulation.

[0109] In order to verify the performance of this method in outlier data screening and fusion in wireless sensor network clusters, the algorithm simulation is carried out on the NS-2 simulation platform. Based on the network model set above, the experiment uses the LEACH protocol to cluster 50 nodes respectively. The basic parameters are set as follows: the simulation time is 160s, one sink node, the initial energy is infinite, the initial node energy is 2J, the node positions are randomly distributed, the rotation period is 20s, and the number of cluster head nodes is 5. After the first clustering, the number of nodes in cluster 1 is 8, and each node generates a set of simulated data (1000) with a mean of 5 and a standard deviation of 0.3 within 20 seconds as the samples collected by the nodes.

[0110] figure 1 is the data sample produced by node 1 in cluster 1. The particle swarm optimization algorithm based on ma...

Embodiment 2

[0114] Embodiment 2: Energy consumption analysis of outlier detection.

[0115] In order to further demonstrate the superiority of the outlier data detection algorithm proposed by the present invention, the method proposed in this paper and the adaptive weighted fusion algorithm are compared in terms of energy consumption on the NS-2 simulation platform. During the execution of the experiment, the energy of ordinary nodes is set to 1J, the energy of sink nodes is set to 100J, all common nodes are randomly generated and distributed in a grid area of ​​100×100, the sink nodes are set in the center of the area, the rotation period is 20s, and the simulation time is 200s, and the wireless communication distance is 100m. In the experiment, the number of ordinary nodes is 50. The new method and the old method are used to simulate, and the average energy of the entire network is calculated every 10s. The specific experimental results are as follows Figure 4 shown.

[0116] It can b...

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Abstract

The invention relates to a wireless sensor network outlier data self-adaption detecting method based on entropy measurement. The wireless sensor network outlier data self-adaption detecting method is characterized by comprising the following steps of 1, network model building: if IV sensor nodes are randomly deployed in a square region A (1*1), a sensor network is divided into a plurality of clusters, one cluster comprises one cluster head and a plurality of cluster member nodes, cluster members are only in charge of collecting and transferring data, the cluster head has the functions of a sensor node and also manages the members in the cluster, and the following network model is built. The wireless sensor network outlier data self-adaption detecting method has the advantages that the concepts of node information entropy and two-dimensional information entropy in the cluster are provided, and the node information entropy and the two-dimensional information entropy can be used as statistical characteristic quantities for respectively describing the information quantity level of global data in the cluster and single node data.

Description

technical field [0001] The invention relates to a self-adaptive detection method for wireless sensor network outlier data based on entropy measurement. Background technique [0002] Wireless sensor network (Wirless Sensor Network, WSN) is composed of a large number of cheap, miniature and energy-saving sensor nodes deployed in the monitoring area. It self-organizes to form a network system through wireless communication. Overlay information on sensing objects in the area, receive commands and exchange information about the real world with the control center. At present, wireless sensor networks have been widely used in various fields such as agriculture, industry, military, and national defense, such as agricultural planting, industrial site monitoring, climate monitoring, earthquake early warning, medical alarm, etc. [0003] The wireless sensor network contains a large number of sensor nodes and a small number of aggregation nodes, which are limited by the computing power...

Claims

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Application Information

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IPC IPC(8): H04W84/18
Inventor 李怀俊
Owner GUANGDONG COMM POLYTECHNIC
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