Designing method for detecting sensor network abnormal data based on space-time correlation

Abstract

The present invention relates to the field of information communication, and discloses a design method for sensor network abnormal data detection based on spatio-temporal correlation, which includes space dimension detection and time dimension detection, and can realize the detection and classification of abnormal data in wireless sensor networks. and classification results, can respond to events in the network in a timely manner, and at the same time, for malicious nodes that affect the observation results of the base station by sending malicious data, thereby reducing the reliability of the network, by reducing their reputation in the network, the data will not Forwarding from malicious nodes, if the reputation of the node is low to a certain extent, the node will be blacklisted and its data will no longer be received, so as to achieve the purpose of shielding such malicious nodes and maintaining network security.

Description

technical field [0001] The invention relates to the field of information communication, in particular to a design method for abnormal data detection of a sensor network based on time-space correlation. Background technique [0002] The abnormal data of wireless sensor network is very important for environmental monitoring. In actual situation, there may be two kinds of abnormal data, which are malicious data and event data. Malicious data can reduce network reliability by affecting the observation results of the base station, while event data is an important manifestation of environmental changes. Event data can be used to obtain changes in the monitoring area. How to accurately identify abnormal data and effectively distinguish them, and how to accurately understand the changes in the monitoring area while maintaining network security is a current research hotspot. [0003] At present, the abnormal data detection methods that are widely used are mainly based on statistics ...

AI Technical Summary

Problems solved by technology

Although these methods can determine some anomalies in the sensor network, they only consider a single dimension and ignore the temporal or spatial correlation between data, making the detection accuracy not high enough and unable to accurately distinguish malicious data and events. data

In addition, many existing abnormal data detection methods have high computational complexity and high communication costs, which cannot be applied to wireless sensor networks with limited computing resources and limited storage resources.

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