Design And Implementation Of Devices Monitoring System Based On WiFi Sniffer

In recent years,with the continuous development of Wi Fi technology,more and more devices with Wi Fi function have been applied to people's work,study,life and other aspects.However,most people use text tags or electronic tags to manage devices in the management of Wi Fi devices,which makes it difficult for users to obtain the working state and location information of devices in real time.In order to monitor the working state and location information of Wi Fi devices in real time,this paper uses Wi Fi sniffer technology to realize the intelligent management of Wi Fi devices.Firstly,aiming at the problem that missing Received Signal Strength Indication(RSSI)sequence of Wi Fi devices was be sensed by Wi Fi sniffer,this paper uses Support Vector Regression(SVR)to predict the missing RSSI sequence;secondly,using Long Short Term Memory(LSTM)network to learn the RSSI sequence,which can obtain the terminal localization model to realize the judgement between indoor and outdoor Wi Fi device;finally,a set of devices monitoring system based on Wi Fi sniffer is designed and implemented.Firstly,due to the Wi Fi signal is easily disturbed and the Wi Fi sinffer cannot sense the data of the device at any time,the RSSI data sensed by the Wi Fi sniffer is missing.Aiming at the problem of missing Wi Fi sniffer data,this paper implements a method for predicting missing Wi Fi sniffer data based on SVR,which is used to predict the missing Wi Fi sniffer data.Using the actual sensing data of Wi Fi sniffer compares the prediction performance of SVR algorithm with traditional means method and BP neural network.The experiment shows that the prediction performance based on SVR is better than traditional means and Back Propagation(BP)neural network.The method for predicting missing Wi Fi sniffer data based on SVR can effectively solve the problem of missing Wi Fi sniffer data.Secondly,in order to obtain the indoor or outdoor location information of the device in real time,this paper proposes and implements a terminal localization method based on LSTM and Wi Fi sniffer.In order to judge whether the device is indoor or outdoor,the Wi Fi sniffer is used to sense the RSSI sequence of indoor and outdoor Wi Fi device and LSTM is used to learn the continuous RSSI sequence to obtain the terminal localization model based on LSTM.At the same time,taking the RSSI value of a single time as the input and the localization of its corresponding device as the output,three terminal localization models based on Support Vector Machine(SVM),K-Nearest Neighbor(KNN)and Gradient Boosting Decision Tree(GBDT)are constructed,which are used to compare with the terminal localization model based on LSTM.The experiment shows that the terminal localization model based on LSTM has better performance and its accuracy reached 98.84%,which can be used to accurately and quickly judge whether the terminal device is indoor or outdoor.Finally,combining with the method for predicting missing Wi Fi sniffer data based on SVR and the terminal localization method based on LSTM and Wi Fi sniffer,this paper uses a Wi Fi sniffer to design and implement a set of devices monitoring system based on Wi Fi sniffer.The system is composed of a Wi Fi sniffer,a Raspberry Pi,a local information processing node and Web client.The Wi Fi sniffer is responsible for collecting Wi Fi device data,the Raspberry Pi is responsible for sending the collected data to the local information processing node,the local information processing node is responsible for analyzing and processing the Wi Fi sniffer data and uploading the location information and working state information of the Wi Fi device to the local database server,finally,implementing the monitoring of Wi Fi devices through the Web client.The demonstration application shows that the system can better meet the demand of intelligent management of Wi Fi devices.The devices monitoring system based on Wi Fi sniffer implemented in this paper can monitor the location information and working state of Wi Fi devices in real time.It provides data support for the effective management of Wi Fi devices,breaks the traditional devices management mode and provides support for the intelligent management and development of devices.Figure [48] Table [25] Reference [60]...