Research On Virtual Space Of Wireless Sensor Network System

This issue comes with the wireless sensor network system, which includes temperature, humility and illumination sensor nodes. By way of modeling and analysis with sensor data in the virtual space, in a hand, users can not only learn about the real-time sensor data collected by sensor nodes and wireless network topology graph, and can predict the development of the environment monitored by system; in the other hand, users can troubleshoot sensor nodes to avoid irreparable damage.Based on the features of wireless sensor networks and applications, this paper focuses a comprehensive in-depth analysis and research on the virtual space. To achieve the observability, predictablility and fault diagnosis, a research for the virtual space technology of wireless sensor networks is designed.Firstly, the research and implementation of observable technology. After the need analysis of the system, the technology can do:the sensor data collected by sensor is presented in graph form to users; the real-time dynamic network topology graph is presented to users in form of tree; provide the energy curve of sensor nodes to users.Secondly, the research and implementation of predictable technology. Owing to the sensor data, such as, temperature, humidity and illumination, has strong regularity, cluster-regression model is proposed and the forecast through the sensor data is reflected the environmental changes. According that the regularity among the energy data of sensor nodes is not strong, the gray prediction model is raised, and the forecast accuracy of the power consumption of sensor nodes is higher.Thirdly, the research and implementation of fault diagnosis technology. For the study of characteristics of the system, the fault diagnosis in research mainly includes the hardware fault caused by the power supply module for sensor nodes, and the soft fault caused by the unusual sensor data collected by sensor nodes. To some extents, users can learn diagnose power fault by the gray prediction model; And as to the analysis about abnormal sensor data, wavelet neural network model is proposed in this paper, and the model can diagnose the abnormal sensor data soft fault in a timely and more highly accurate manner.