Identification of Sensor Node Localization Anomalies in Parallel Big Data Networks
Parallel big data sensing networks typically have complex topological structures and a large number of nodes,which can affect the reliability of identifying abnormal nodes in the sensing network under big data interference and complexity interference.To this end,a method for identifying sensor node localization anomalies in parallel big data networks is proposed.Boolean model is established to calculate the coverage radius of the node transmission channel,the characteristic parameters in the transmission channel are estimated based on carrier modulation methods,a communication transmission channel model between sensor nodes is estimated,and the node sta-tus and distribution are obtained.The distributed sensor sequence sampling model is constructed,the sensor node feature sequence is collected,and the compressed sensing method is used to identify the anomaly of sensor node positioning.The simulation results show that the energy consumption of the proposed method for identifying abnormal nodes is always lower than 3 J,and the average recognition error does not exceed 0.35％,the node positioning accuracy is higher than 95％,and the running time for identifying sensor abnormal nodes is less than 2 ms,which can effectively extend the lifespan of parallel big data networks.
sensing nodesidentification of positioning anomaliescompressed sensingparallel big data networkboolean modelfeature collection
万方数据
维普
