WSN (Wireless Sensor Network) anomaly detection method based on MEA-BP neural network

Abstract

The invention discloses a WSN (Wireless Sensor Network) anomaly detection method based on an MEA-BP neural network. The method comprises the following steps: initializing various distributed sensor nodes, and starting to acquire data by various sensor nodes; using a K-means algorithm to perform space clustering on the various sensor nodes to obtain a plurality of cluster structures; using a mind evolutionary algorithm to perform parameter optimization on a BP neural network, optimizing the weight and threshold of the BP neural network through a convergence and dissimilation operation, obtaining optimal weight and threshold, inputting the optimal weight and threshold, and establishing an MEA-BP neural network model; and adopting a distributed algorithm to execute anomaly detection on the sensor nodes in each group of clusters independently, after anomaly detection is finished, transferring a detection result to cluster head nodes of the group of clusters for further verification by the sensor nodes. The WSN anomaly detection method based on the MEA-BP neural network provided by the invention improves the algorithm performance of the BP neural network, accelerates the learning rate of the BP neural network, effectively improves the accuracy of the abnormal data detection and reduces the false positive rate.

Description

technical field [0001] The invention belongs to the technical field of wireless sensor network (WSN) data reliability detection, and in particular relates to a WSN anomaly detection method based on MEA-BP neural network. Background technique [0002] As a wireless self-organizing network, wireless sensor network (WSN) has the characteristics of low energy consumption, flexible nodes, even no manual maintenance, and can work for a long time in harsh environments. By distributing sensor network nodes in the target monitoring area, collecting environmental data and monitoring specific events is one of the most common applications at present. Due to the limited resources of wireless sensor nodes, and they are easily disturbed and destroyed by external factors, or affected by external environmental emergencies, the data collected by the nodes may have obvious deviations from the environmental characteristics under normal conditions. This type of data is called abnormal data. Th...

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Problems solved by technology

[0004] Purpose of the invention: In order to overcome the deficiencies in the prior art, the present invention provides a WSN anomaly detection method based on the MEA-BP neural network, aiming at the problems that the BP neural network algorithm is easy to fall into a local optimal solution, long training time, and low efficiency , using the thinking evolution algorithm to improve the BP neural network, improve the performance of the BP neural network algorithm, accelerate the learning rate of the BP neural network, effectively improve the accuracy of abnormal data detection, and reduce the false alarm rate

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