Based On The Time Period The Wireless Sensor Network Data Fusion Energy Saving Algorithm

With the development of the on-chip system, microelectromechanical systems and wirelesscommunication technology, wireless sensor networks technology can be achieved. Because of itssmall size and easy-to-deploy, wireless sensor networks develop rapidly. It has widespreadapplications, for example, military field, ecological and environmental monitoring, medical field,road condition monitoring. Sensor nodes energy supplied by the battery is not easy to replace,the limited supply of energy is a major feature of wireless sensor. How to make more effectiveuse of limited energy and prolong network life is the key issues for wireless sensor networkresearch.In this paper, based on the time period the wireless sensor network data fusion energysaving algorithm is to conserve energy, from the point of the data processes in order toprolong the life of networks. After sensor nodes are deployed completely, according to a certainway it organize ad hoc networks. Sensor nodes collect information, and then transfer thecollected data to the sink node through the network. While the sensor continuously detect thetarget, there may be an association between the collected data. Some shows a certain periodicity,for example, a few days of temperature data. According to the data association establish aforecast model. The model is synchronism to ordinary nodes and sink node. According to theuser needs, we set the threshold value, which is the deviation between the allowable deviationand the predicted. Common node predict the data which is going to be collected. If the predictiondata and the deviation of the measured data within the error range, it is not necessary to uploaddata, otherwise to upload data and update the prediction model. The sink node waits for uploaddata. If there is upload data, it saves data and updates the forecast model, otherwise according tothe collected historical data to predicts future data. In the working process it maintainssynchronization prediction model in ordinary nodes and sink node. In this paper, I havesimulated the above algorithm, using data fitting method. Analysising the simulation results, itshows that this algorithm saves node energy and improve the network life cycle.The algorithm reduces the amount of data upload through the prediction of the future data.saving the energy of the sensor note which consume from the sensor node’s transmission data.And it prolongs the life of the network.