Node Anomaly Detection And Energy Consumption Optimization In Wireless Sensor Networks

Wireless Sensor Network(WSN)is a non-wired data collection,analysis,sorting,and transmission network.It has become one of the most important technologies in the 21 st century due to its reliable remote monitoring capabilities,self-organization,flexible configuration,and convenient expansion.Because wireless sensor networks are usually deployed in harsh,dangerous,and inaccessible or complex or even expensive environments to perform information collection operations,wireless sensor networks cannot have continuous energy support,which may be affected by network fault tolerance,energy consumption,and the limitations of the network life cycle face huge challenges.Therefore,it is very important to establish a fault detection mechanism for wireless sensor network nodes so that the network can perform self-diagnosis of faulty sensor nodes,reliable data recovery,and optimization of network energy consumption.This article makes the following work according to the requirements of the above three aspects:(1)To improve the fusion algorithm of the spatial-temporal correlation of wireless sensor network,elaborates the concept of correlation and correlation coefficient in detail,and three correlation coefficients were introduced.In the improved fusion algorithm,autocorrelation coefficient and Pearson correlation coefficient were used as the benchmark parameters to measure the spatiotemporal correlation of sensor data in wireless sensor networks.In the process of abnormal data recovery,exponential smoothing algorithm and piecewise least square algorithm were introduced to improve the prediction accuracy of abnormal data recovery algorithm.Finally,the improved algorithm was compared with the single time correlation algorithm,spatial correlation algorithm and the improved algorithm in node fault detection and abnormal data recovery.(2)To improve the fault detection rate and the accuracy of data recovery,the concept of graph signal was introduced,the algorithm of fault detection and abnormal data recovery of wireless sensor networks was improved based on spatiotemporal correlation,and combined graph signal processing with sensor node fault detection and abnormal data recovery algorithm based on spatiotemporal correlation.Firstly,the abstract graph was created according to the wireless sensor network,and then the low-pass filter was used for fault detection.Then,according to the temporal and spatial correlation of sensor node data,the graph signal was recovered on the basis of the graph Fourier transform and inverse transform,so as to achieve the purpose of abnormal data recovery.(3)Aiming at the problem of energy consumption in wireless sensor networks,several traditional energy consumption optimization algorithms were compared and improved.The simulation results show that the network life cycle can be effectively extended.In the process of network clustering,the improved DEEC clustering algorithm was adopted,which increased the weight to affect the cluster head election probability.The distance weight value was introduced to optimize the performance of network clustering,so as to ensure the reasonable clustering effect,adjust the energy consumption of each sensor node in WSN,maintained the complete network topology,and increased the service life of the whole network.From the simulation results,it can be seen that the improved WSN node fault detection algorithm studied in this paper can maintain excellent performance in the overall aspects of sensor node fault detection,abnormal data recovery and energy consumption optimization,which has certain practical significance and engineering value.