Research On Data Fusion Algorithm For The Wireless Sensor Network

Wireless sensor network is a data-centric networks, including the technology of sensor, wireless communication and computer network. It is widely used in commercial, industrial and military fields and has become a hotspot research field both at home and abroad. However, due to the fact that sensor nodes is limited in energy and is unable to replenish energy automatically, to prolong the lifetime of the wireless sensor network has been a hot research topic so far. At the same time, some related researches show that most energy of sensor nodes wasted in data transmission, while the data fusion technology can effectively reduce redundant data in sensor networks, which helps to obtain more accurate information.At present, most of the data fusion algorithm aims at dealing with the data in one or more acquisition cycles, when abnormal situation occurs in the sample period, the unusual information won’t get timely response handling without corresponding processing method. In this paper, having made a further investigation of these fusion algorithms, and proposed two intelligent forewarning fusion algorithms, which based on BP neural network under the single-dimensional and skyline beyond the multi-dimensional, respectively. Those two kinds of fusion algorithm has the following innovation points:First, we append the intelligent forewarning mechanism to the data collecting process of nodes, handling the outliers in time; Second, we put forward the fusion algorithm with data preprocessing, cluster member nodes on the acquisition of data preprocessing, while cluster head nodes obtain the fusion results by the fusion algorithm; Third, under the single dimension, we present a diversification data fusion model with BP neural network, so as to meet the needs of various customers; Fourth, under the multi-dimensional, we also proposed a data fusion algorithm based on Skyline queries, according to the actual needs of users, optimizing the Skyline query results. Simulation results show that the preprocess algorithm and fusion algorithm is efficiency in operating, scarce in fusion results error, less in the amount of data transmission and lower in energy consumption, Besides, it could response the abnormal cases in sampling period of anomalies in time.