Wireless sensor network fault diagnosis method

Abstract

The invention discloses a wireless sensor network fault diagnosis method, which comprises the following steps of: acquiring link quality signals of a wireless sensor network in a normal state and a fault state; performing wavelet packet decomposition and coefficient reconstruction by using a wavelet transform method, and extracting a characteristic vector of a fault; constructing a coding matrix by adopting an error correcting output coding method to realize network fault multi-classification; constructing a decision function of multi-classification problems by using an LSSVC learning mechanism, and establishing a relationship model between the system state and the characteristic vector; and identifying, diagnosing and processing a generated fault, a potential fault type and an area according to a network running decision function value. The method has wide application prospect in small and medium-scale WSN application system.

Description

technical field [0001] The present invention relates to a wireless sensor network fault diagnosis method, especially a wireless sensor network fault diagnosis method based on wavelet packet transform and least squares support vector classifier (LSSVC), which is used to diagnose the faults and potential fault types of WSN The invention relates to identifying and diagnosing an area and an area, and belongs to the technical field of wireless sensor and network fault diagnosis. Background technique [0002] With the application of wireless sensor networks more and more widely, in order to ensure the reliable and stable operation of the network and maintain system resources, it is necessary to establish a fault diagnosis mechanism. At present, the research on fault diagnosis methods of WSN network mainly focuses on the diagnosis of node faults (i.e. hard faults). The related researches at home and abroad are as follows: [0003] Foreign studies include: in 2002, S.Chessa, P.Sant...

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Problems solved by technology

[0004] Domestic research includes: In 2007, Lei Lin et al. proposed a fault diagnosis method for WSN nodes based on Rough set (rough set) theory, using rough set theory to simplify fault attributes, reducing network overhead in the diagnosis process, and querying nodes through message inquiry. proposed a WSN fault diagnosis algorithm based on the comparison of cluster nodes, using the cluster head as the centralized control unit for fault diagnosis in the cluster to perform centralized diagnosis on the nodes in the cluster, and at the same time using the logic linking all cluster heads The subnetwork diagnoses the gateway by passing relevant diagnosis information between the cluster heads; Gao Jianliang of the Chinese Academy of Sciences considers that the wrong measurement data in the WSN will lead to the degradation of network service quality and energy waste, and proposes a method of fusing neighbor nodes. In 2008, Yang Yun proposed a WSN inconsistency fault detection mechanism IFDM (Inconsistent Failures Detection Mechanism) for the inconsistency problem in the WSN data transmission process; Li Qianmu et al. Research on network status and fault indicators, and propose a fault diagnosis method based on Dempster's rules (DRNFD). Applying DRNFD to diagnose faults can effectively reduce the rate of false alarms and false alarms, making real-time fault diagnosis possible

[0005] From the perspective of research status, most diagnostic algorithms are system-level diagnostic algorithms for node faults, which require frequent exchange of diagnostic information between nodes, and the energy consumption of nodes is large, which has a great impact on the normal low-power operation of the network.

Moreover, most of the current research is aimed at large-scale WSNs, and there are few researches on fault diagnosis of small and medium-sized WSNs, which is far from meeting the needs of WSN technology development.

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