Wireless Sensor Network Data Fusion Algorithm

Wireless Sensor Networks(WSN), as an information collection platform, is developing rapidly in recent years, which can monitor and collect various data about the test object in real time in the region where the net achieve, and send them to the network gateway node, to realize the target detection and tracking within the specified range.This paper analyzes the characteristics of wireless communication. As in traditional wireless sensor networks, the routing algorithm is hard to reset, the power is limited and blind spots are existed, we design an improved clustering multi-hop wireless routing algorithm. Before the cluster formation stage, we divided the monitoring region into many small regions, and choose the cluster head node taking the residual work time as the primary indicator. There is an optimum value when the node enerty consumption meet the energy consumption model, and divide the region into cluster on the basis of maximum entropy clustering method. The nodes in each cluster select the specific cluster transition through the information redundancy after cluster. The simulation conclude that the algorithm performs monitoring well in term of improving the effective rate of data, extending the network life cycle, eliminating blind sports and save the network energy consumption.The data fusion is an important way to solve the redundant data in wireless sensor networks. The main idea is integrated different data from multiple information sources, according to the characteristics of the user data, remove the redundant information and reduce the amount of transmitted information. By this way, the conflict between data transmission can be reduce and improve the effeciency of data collection. In this paper, we use clustering methods to achieve data integration. By analyze the current various clustering methods, propose an data fusion method by improve the constraints.By the simulation analysis, it can prove that the new routing algoritnum and data fusion algoritnum has good improvement in data error rate, and integration of precision has also been significantly improved, all nodes take turns to handle the data as a cluster head node to the network Consumption is more balanced and effective in prolonging the network life cycle.