Research On Fault Diagnosis Of Wireless Sensor Networks Containing Disturbances

Wireless Sensor Network(WSN)is one of the most influential key technologies in today's society,and has been widely used in many fields such as environmental monitoring,industrial control and smart home.WSN is constructed by a large number of nodes through self-organization,and integrates wireless communication technology,sensor technology and distributed information processing technology to achieve the integration of the physical world and the information world.WSN has the characteristics of large number of nodes,wide distribution range,limited power source energy,and complex application environment.Under the interaction of internal and external factors,WSN nodes are more prone to failure than traditional networks.A large number of node failures will cause WSN to lose connectivity.Sex.Due to the disturbance during WSN operation,it is impossible to determine whether it is caused by a fault or a disturbance when data fluctuations occur.Therefore,WSN fault diagnosis research with disturbance has important theoretical significance and application value.This thesis focuses on fault diagnosis for wireless sensor networks with disturbances.The main work includes the following aspects:First,this paper uses a BRB-r method to detect WSN faults.This method uses the Belief Rule Base(BRB)of the reliability factor,extracts the reliability factor through a statistical method,and uses the evidence-based reasoning algorithm.Reliability factors are fused and a BRB-r model is constructed.The BRB-r model is used to compare and analyze the observation data of the nodes in the cluster of the wireless sensor network,find out the abnormal data,and judge whether the WSN is faulty.Secondly,a hierarchical BRB method is used to analyze and classify the fault data.The hierarchical BRB model is a complex BRB model that is transformed into multiple simple BRB models to solve the rule explosion problem.This paper uses a two-layer BRB model.The output of each BRB is defined as a different fault type,and the premise attributes of the fault data are used as the input of the first-level BRB model,and the input of the second-level BRB model is the output of the first-level BRB model.A hierarchical BRB fault classification model is used to determine its fault type.Finally,numerical experiments were performed on the WSN data set collected by the Intel Berkeley Research laboratory.The premise attributes of the data were extracted,and the hierarchical BRB-r model was used to conduct experimental research on WSN fault diagnosis in a disturbance environment.The hierarchical BRB was verified.Effectiveness of the BRB-r method for fault diagnosis in wireless sensor networks with disturbances.