The Research On The Data Gatherhing Algorithm In Wireless Sensor Networks

Wireless sensor networks (WSN) consists of a large number of sensor nodes which integrate sensor, the unit for data processing and the module for the short distance wireless communication. The networks is a kind of wireless self-organizing networks with a distinct data-centric property, and the objective of sensor networks is to sense, collect and process the information of the various environments or the detected objects in the coverage area of the networks, and then send the sensing information to the user. Wireless sensor networks, together with utility computation, plastic electronics and body bionics, is considered as the four high-tech industries in the future around the world. As a new generation of the detecting and control system, the data gathering in sensor networks is the most fundamental and important function in the network system, and the performance of the data gathering mechanism directly influences the energy conservation in sensor networks.The research of the dissertation focuses on data gathering and node scheduling mechanism in sensor networks. Based on the systematic analysis of the present data gathering and node scheduling mechanisms in sensor networks, the new data gathering and node scheduling mechanisms are presented with the consideration of saving the energy resource in networks to the largest extent. And the mechanisms would perfect data gathering research in sensor networks. The main work is as follows:First, the dissertation summarizes the data gathering mechanisms in wireless sensor networks, and based on the model about the energy consumption in sensor networks, analyses the mechanisms on the tree-based level and the cluster-based group data gathering in detail. The dissertation also analyses data aggregation in the course of the data gathering and data prediction on the basis of data correlation in sensor networks.Second, in order to make the Sink node gather the data from different locations within the detceted area of the networks, the dissertation puts forward the classifying and predictive data gathering algorithm which is based on the property of data correlation in networks. The algorithm maps data classification to partition of the sub-area in detected area and completes the data gathering by means of selection of the representative nodes and data prediction mechanism in sensor networks. The theoretical analysis and the simulation test the advantages and reliability the algorithm.Third, the dissertation analyses node scheduling mechanisms in sensor netwoks. Based on the models of the selective scheduling in time slots and the sensing data scheduling, the paper puts forward node scheduling algorithm based on detected probability of the sensing data and energy-balance of the nodes. The algorithm solves the scheduling conflict by introducing the back-moving mechanism of the binary system and realizes the node scheduling by the computation of the the detected probability of the sensing data and residual energy. The validity of the algorithm has been tested by theoretical analysis and the simulation.