Study On Node Data Management And Energy Consumption Of Wireless Sensor Networks

Wireless Sensor Networks (WSN) is a new type network made up of sensor, network and wireless communication technologies and has a wide application future, and it is one of the new and high technologies in the 21st century.The nodes of WSN are extremely power constrained, so energy-efficient data management and prolonging the networks lifetime are the major concerns to many scholars in this researching area. Each node of WSN is an independent computing and controlling unit which can achieve their own data management such as sensing data, analyzing data, transmitting data and controlling their own state, or collaborate with other nodes for the data management. Node data management is a part of WSN data management, and it is closely related to network topology, node characteristics and node sensing data. How to effectively manage nodes'data is very important to improve the networks energy efficiency and extend the networks lifetime, and there is little research result for WSN node data management.Through energy-efficient node data management, the reliable data can be provided to the users, and the unnecessary energy consumption for broadcasting message and transmitting data can be reduced, and then the entire network energy efficiency can be improved significantly and the network lifetime can be extended. In this paper, the technologies of node data management and energy consumption are studied mainly from the characteristics of the node data management, network topology control and node classification management and sensing data prediction of nodes. The specific studies are as follows.â‘ According to the characters of WSN and its nodes, the relationship between the WSN data management of and the node data management are discussed. The good topology management, correct processing of the node data and the rules of scheduling node are very important to the node data management and energy consumption optimization. The main studies of node data management and energy consumption optimization are discussed.â‘¡By analyzing the advantages and disadvantages of the existent clustering algorithms, a new clustering algorithm based on optimum parameters is presented. The relationships of the optimum one-hop distance and clustering angle with the nodes electronic parameters and the number of the total nodes are given for minimizing the energy consumption between inter-cluster communications. Furthermore, the continuous working mechanism of each cluster head which acts as the local control center and will not be replaced by the candidate cluster head until its continuous working times reach the optimum values is given. The simulation results demonstrate that the presented clustering algorithm can effectively reduce the energy consumption used for intra-cluster broadcasting message and gathering data, and prolong the network lifetime.â‘¢With calculation and analysis function of node, a node classification algorithm based on the integrative supportability of sensing data is presented. Each cluster head analyzes its member data correlation using error function and fuzzy function, and gains the integrative supportabilities of its members'sensing data. Based on the integrative supportabilities, the members of the cluster are classified as conflict nodes, complementary nodes and reliable nodes. The sleeping rules are given according to the node's integrative supportability and its increment, and the controlling rules for the redundant nodes with high integrative supportabilities are given to reduce the energy consumption of the cluster and balance the energy consumption among members. The simulation results demonstrate that the presented algorithm can realize the node classification, reduce the amount of the data transmission and prolong the network lifetime.â‘£In order to reduce the amount of data transmission, a node data prediction algorithm for the data collection is presented. The cluster head predicts some members sensing data with the basic GM (1, 1) prediction model and the mechanism of dynamically updating array parameters. According to the different predicted modes of the cluster head, the two predicted scheduling mechanisms contained ordinal scheduling and selective scheduling are presented and the data fusion algorithm is given. The test and simulation results demonstrate that the presented algorithm can accurately predict the node sensing data, improve the energy efficiency and prolong the network lifetime.The last chapter concludes the presented algorithms and the related work for the study of the node data management and energy consumption, and outlines the further research contents and directions.