Researches On Efficient Data Gathering And Retrieval Algorithm For Wireless Sensor Networks

Wireless Sensor Network (WSN) has such features like low cost, flexible, and self-organization, it can be used widely in many areas like military, environment monitoring, healthcare, business, and space exploration. However, the traditional sensor network node data gathering algorithm is complex, and its routing binding effect is not ideal, and it cannot satisfy the requirements of high energy efficiency data collection of wireless sensor network, as well as complex data query and attribute decision problems. Thus an urgent research is needed on wireless sensor networks’ data collection, query, in order to widen the sensor network applications.Routing in dynamic sensor network can improve the efficiency of energy consumption of the sensor nodes in certain degree for data forwarding. However, the complexity introduced by the mobility of sink nodes in dynamic sensor network makes it difficult to maintain a transmission path among the multiple nodes. Therefore it is necessary to select the sink path carefully in order to meet requirements of both high efficiency and low power consumption in dynamic sensor networks. To solve above problem, a new algorithm for selecting the moving path of sink nodes under the constraint dynamical sensor network is proposed. It builds a graph model of mobile sink nodes within limited area under the communication mode of auxiliary Cache nodes. According to different applications, it is also discussed about the optimal mobile path when the global path information of mobile sink is available and when the local information is known. When the global path information is given, it adopts the method based on Vornon Partition with energy cost and node load. When the local information is known, it adopts heuristic strategy to seek the optimal path, and it also proves the validity of the method. The simulation experiments results show the effectiveness and feasibility.Based on the routing selection, a new data gathering algorithm is proposed by leveraging the advantages of compressed sensing like fault tolerance and simple coding. This algorithm combines Bayes compressed sensing theory with sensor routing, and has solved the problem of existing algorithm, i.e., the sensor is sensitive to energy consumption. The basic idea of the algorithm is to firstly seek energy efficient and appropriate routing optimal projection according to the initial observation vector, and secondly use the node coefficient of minimum energy consumption and the principal component of generalized vector as the target node and the principle of maximum differential entropy change for node optimal projection coefficient, and lastly use reverse multicast routing structure in the problem of sink and the target node routing. Theoretical and simulation results indicate that it has obtained better reconstruction effect of simulation under the condition of ensuring energy consumptionOn the extended study of the nodes energy-efficiency and skyline query of the node position attribute decision problem existing in wireless sensor networks, an algorithm about data annular areas query processing is proposed. With this algorithm, the entire area is separated into several data annular by query centered position (p). When query k skyline data of the close query position (p), by pruning strategies only conducting compare with the other attribute values whose distance is less than p, it helps to reduce the data scope and raise the query efficiency. Furthermore, with k-skyline queries in different annular sub-areas can be processed with serial or parallel ways, the query cost and the query delay are both improved. Experiments have showed that this algorithm has less query cost and the query delay when compared to Flooding algorithm and TAG algorithm.