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An Improved WSN Data Integration Scheme Base on BP Neural Network
Youwei Shao 보안공학연구지원센터 2016 International Journal of Future Generation Communi Vol.9 No.9
In forest fire monitoring, in order to achieve the goal of reducing a large number of invalid and redundant data in wireless sensor network, improving the convergence rate of the wireless sensor network, prolonging the life cycle of nodes, improving the accuracy of fire report, this paper proposed an improved data integration method based on BP neural network. Data generated by various sensors can be integrated on the nodes with this method, the convergence speed of BP neural network can be improved by reference of real-time processing capacity of the node, and thus the energy consumption was reduced to a great extent. The experimental results showed that the proposed method can be well applied in fire monitoring sensor network, the monitoring accuracy was improved and the energy consumption of nodes was reduced, the capacity of wireless sensor network for forest fire monitoring was increased significantly.
Youwei Shao 보안공학연구지원센터 2016 International Journal of Multimedia and Ubiquitous Vol.11 No.8
Task scheduling and resource scheduling are the core issue in cloud computing. Pointing at the premature problem in the scheduling algorithm of particle swarm, we propose a scheduling algorithm of cloud task particle swarm based on “fission” mechanism in this paper. The particle in traditional particle swarm algorithm gets “fission” by the new algorithm in appropriate place, to get more kinds of the particles, contributing to the particle swarm diversity, avoiding premature convergence of the swarm. As the experimental result shows that, the algorithm in this paper has faster scheduling efficiency than the FIFO and the PSO, also solves the premature problem in PSO.
Parameter Estimation of Low Intensity Wireless Network Receiving Signal Spectrum
Youwei Shao 보안공학연구지원센터 2016 International Journal of Future Generation Communi Vol.9 No.10
For subnanosecond low intensity of wireless network signal with representative Beidou Satellite navigation signal is difficult to obtain accurate time estimation and Angle estimation in the positioning, and easy to be affected by environmental noise, which made its positioning accuracy not higher, this paper proposed a parameter estimation method based on low intensity wireless network receiving signal spectrum. By sampling approach, first sampling launch signal as multidimensional independent sub-signal and modeling independently, through constructing noise space and sub-single space to obtain TOA estimation accurately based on the orthogonalization of corresponding column vector; then using complex domain mapping, on the basis of obtained TOA estimation, to obtain DOA estimation with contrast way accurately. Finally, it conducted accuracy analysis of the proposed parameter estimation method. Test data showed that: compared with PM algorithm, ESPRIT algorithm, the proposed technique was more accurate on TOAand DOA estimation; and in case of low strength of signal and serious background noise, the proposed method can still be effective to maintain the precision of parameters estimation. The technology can effectively reduce the influence of background noise on the signal transmission, has strong practical deployment significance.
Improved Method of Transfer Close Package for Cloud Resource Clustering in Cloud Computing
Youwei Shao 보안공학연구지원센터 2016 International Journal of Grid and Distributed Comp Vol.9 No.12
A cloud computing resource clustering framework based on fuzzy theory is constructed. There are five steps: characteristics and requirements set, data standardization process, data normalization process, fuzzy similarity matrix, cluster partition. In the clustering partition, the transitive closure method is improved, and the special elements in the fuzzy similarity matrix are used as the control points to simplify the optimization process and improve the optimization accuracy. The experimental results show that the cloud computing resource based on the improved transitive closure method is fast and the resource utilization rate is high after clustering.
Youwei Shao 보안공학연구지원센터 2015 International Journal of Grid and Distributed Comp Vol.8 No.3
A major challenge facing cloud computing is virtual resource allocation with dynamic characteristics. Evaluation of a resource allocation strategy using a single aspect can no longer meet the real world demands. We resolve this issue from the perspectives of users and resource providers using a particle swarm algorithm for resource allocation. With this algorithm, we establish an allocation model using the shortest task completion time and the lowest cost as the constraints. The fast convergence rate of the particle swarm algorithm is then used to find the optimal solution for resource allocation. The velocity weight of each particle is self-adaptively adjusted based on the fitness value of each particle, resulting in an improvement in the global optimization and convergence capabilities. Finally, a simulation with the CloudSim platform shows that this algorithm can take into account the completion time and cost, which ensures the minimum cost in the shortest possible time to complete the task to improve resource utilization.