Wireless sensor network fault diagnosis method based on time weight K-neighbor algorithm

Abstract

The invention relates to a wireless sensor network fault diagnosis method based on a time weight K neighbor algorithm, comprising steps of establishing a K-neighbor algorithm training database, sampling WSN state characteristic value to form characteristic vector through timing discrete, wherein each characteristic vector represents the sampling state of the wireless sensor network, performing a pre-diagnosis on a WSN characteristic vector through the K-neighbor algorithm and starting up a time correlation mechanism, starting up a weight amendment rule if the condition is met, and outputting results. The invention can establish the characteristic value according to the system fault mechanism by targeting the wireless sensor network (WSN) system fault diagnosis problem, and can design the fault diagnosis classification rules and parameters based on the weight according to the WSN system fault time correlation, and can establish a system fault diagnosis model by combining with the K-neighbor algorithm to achieve the fact the current diagnosis result is amended according to the diagnosis history. The invention can achieve the fault self-diagnosis and self updating of the WSN, has distributed calculation characteristics and guarantees the accuracy and low power consumption.

Description

technical field [0001] The invention is a wireless sensor network fault diagnosis method based on the time-weighted K-nearest neighbor method, specifically adopting the time-weighted method to carry out automatic fault diagnosis on the wireless sensor network. It belongs to the interdisciplinary category of wireless sensor network and distributed pattern recognition. technical background [0002] As a research hotspot in the field of automation and artificial intelligence today, the wireless sensor network (wireless sensor network, WSN) is composed of a large number of low-cost nodes. It is a distributed, multi-level, node multi-hop and self-organizing cooperation system. It is widely used in environmental monitoring, agricultural production, intelligent systems, medical care and other fields. WSN detects various environmental physical information through its various topological structures, and its application range is constantly expanding. However, WSN may fail in all asp...

AI Technical Summary

Problems solved by technology

However, WSN may fail in all aspects of collection, processing, data transmission and system coordination, reducing or losing the system monitoring function, and its reliability and stability requirements are increasing day by day

[0003] WSN component failure research is of great significance in the relatively backward stage of hardware technology, but today, with the advancement of chip manufacturing technology, ...

Images