Research On Key Technologies For Distributed Data Gathering In Wireless Sensor Networks

The optimization problem of the data gathering has been proposed as an essential paradigm for most the event monitoring applications in wireless sensor networks (WSN). The data gathering mechanisms are aimed at reducing the energy consumption of the network nodes and at the same time maximizing the network lifetime, so as to avoid the immense cost of redeployment for satisfying the demands of the monitoring system. Therefore, technologies of data gathering in WSN are systemically and deeply investigated in this dissertation. The research work and main contributions are as follows:Depending on the event monitoring application in WSN, the existence of the sensed information with spatial and temporal correlations bring significant potential advantages for the development of efficient data gathering strategies well suited for the WSN paradigm. In order to decrease the amount of data transmission in network and reduce the energy consumption, an energy-efficient linear regression based distributed data gathering optimization strategy was proposed. The linear regression model can accurately represent the feature of the original monitoring data. Rather than transmitting measurements to another node, nodes communicate constraints on the model parameters in the error bound, drastically reducing the communication cost. In addition, the linear regression model of the sensed data not only has advantage of low computational complexity but also is suitable for the incremental updating. The theoretical analysis and experimental results show that the proposed data gathering strategy is able to implement measurements prediction and estimate with lower communication cost. The designed algorithm achieves more energy savings and extends the wireless sensor networks lifetime.The data gathering optimization of the large-scale, collaborative and concurrent multi-task in WSN is very important, especially in the environments where multiple geographically overlapping wireless sensor networks are deployed. For decreasing the deployment cost of independent sensor networks each dedicated to a specific task, the virtual sensor network (VSN) based data gathering optimization algorithm for the concurrent application was proposed. The strategy builds a hierarchical structure by distributed clustering technique on the WSNs before forming virtual sensor network in logical to meet various monitoring requirement from different kind of application deployment. Then, for the data gathering on the VSN framework, the CH nodes set and update hierarchical thresholds by using the MinMax operator to restrict the data transmission. In order to enhance the robustness of the framework, a fault-tolerant strategy for VSN based on statistical hypothesis testing was proposed. On the cluster-tree based VSN architecture, the fault occurring was judged by checking the matching degree between local data reading sequence and event statistical characteristics depend on the spatial and temporal correlations of sensed data. The simulation results show that proposed algorithm achieves more energy savings and extends the wireless sensor networks lifetime. In addition, it can maintain the fault identification rate and event region monitoring probability at the satisfactory level with increase of the fault sensor nodes probability.Aiming at solving the transmission of the multimedia information in wireless multimedia sensor networks (WMSN) applications, which require both energy efficiency and Quality of Service (QoS) assurance, a multiple QoS metrics hierarchical data gathering algorithm based on swarm intelligence optimization for WMSN was proposed. In order to decrease runtime of basic artificial fish swarm optimization (AFSO), a dynamic artificial fish swarm optimization based cluster algorithm was present. The algorithm achieve more appropriate cluster and better global/local optimization through dynamic adjusting visual and step parameters of artificial fish. The2ASenNet (combination of improving ACO and AFSO) built a cluster head communication tree structure to meet various QoS requirements. Then, the2ASenNet adopted hybrid and behavior to produce diverse original paths, adding AFSO to ACO’s per iterative process, and the optimization path was explored according to multiple QoS constrained. The simulation results show that proposed algorithm can satisfy the multi-format, multi-attribute and multi-mode data transmission needs of so many different applications based on multiple QoS metrics.In order to extract detailed information about the environment, a mass of high complexity and high dimensional nonlinear data information were collected by wireless multimedia sensor network nodes. A nonlinear dimensionality reduction algorithm of multimedia data based on locally linear embedding (LLE) was proposed due to the failure clustering using the previous linear strategies. LLE can recover global nonlinear structure from locally linear fits. The each sampling data point and its neighbors lie on or close to a locally linear patch. The local geometry of these patches was characterized by linear coefficients for reconstructing each sampling data point from its neighbors. The reconstruction errors are measured by the minimizing cost function. Aiming at solving the failure problem of LLE when the source data is sparse, the locally linear approximating (LLA) based nonlinear dimensionality reduction algorithm of multimedia data algorithm was proposed. It reaches the aim of locally linear approximating through adopting the way of direct gradients estimation, thus realizes the dimensionality reduction of the high dimensional nonlinear data. The simulation results show that the proposed algorithms can preserve the original geometry topology structure and extract the most intrinsic character embedded in the high dimensional data space. The dimensionality reduction result of LLA algorithm has significantly improved compared with the LLE when the source data is sparse.The above contributions are of great significance to decrease the total energy consumption, prolong the network lifetime, enhance the collaboration of the sensor nodes, improve the dependability of the data gathering algorithm and reduce the dimensionality of the sensored data in the key technologies for WSN data gathering.