Wireless sensor networks are featured by restricted network resources, which is quite possible to result in low positioning precision and serious time delay in positioning, accordingly, the overall network positioning quality may be reduced; to improve the positioning precision of WSN, based on the DV-HOP positioning algorithm, two aspects of the node positioning were improved from the error precision and least square estimation; thus, a WSN positioning algorithm based on 3D discrete chaotic mapping was proposed: first, a 3D discrete chaotic mapping was constructed, the Chaos Optimization Algorithm was introduced into the positioning error precision calculation, and the unknown nodes were positioned by introducing the least square estimation; second, a simulation experiment of new algorithm was performed from the aspects of communication radius and topological structure. The experimental results showed that the algorithm proposed in this paper can effectively reduce the positioning error caused by calculation and improved the positioning precision. Further, based on the algorithm of this paper, the moving mechanism could be introduced to make dynamic planning for overall network resources, so that the energy cost of the algorithm of this paper in the confirmation process of WSN network terminal could be further reduced to make the algorithm more valuable in engineering field.
College of Computer Science, Inner Mongolia University, Hohhot, China
Internet of Things;Big data;Real time;Cluster storage;Optimization
Data storage, especially big data storage, is a research hot spot in Internet of Things (IoT) system today. In traditional data storage methods, the fault-tolerant algorithm for data copies is adjusted with whole data file, which causes huge redundancy because there are less utilization and more cost of data storage when only a part of data blocks in the file are accessed. Therefore, an optimized cluster storage method for big data in IoT is proposed in this paper. First, weights of data blocks in each historical accessing period are calculated by temporal locality of data access, and the access frequencies of the data block in next period are predicted by the weights. Second, the hot spot of a data block is determined with a threshold which is calculated by previous data access. Meantime, in order to improve the data access efficiency and resource utilization, as well as to reduce the copy storage costs, copy of data block is dynamically adjusted and stored in different groups with high-performance and low-load nodes for data balance. Finally, experimental results show that the storage cost of proposed method is 70% less than that of traditional methods, which means that the proposed method effectively improves the data access speed, reduces storage space, and balances the storage load.
International Journal of Advancements in Computing Technology,2011年3(10):291-298 ISSN：2005-8039
[Jin, Huixia] Department of Physics and Telecom Engineering Hunan City University, Jinhuixia, China;[Deng, Xiaojun] College of Computer and Communication Hunan University of Technology, Zhuzhou, 412008, China;[Tu, Li] Department of Computer Science Hunan City University, Yiyang 413000, China
Image enhancement is even more important in the image processing field. The goal of image enhancement is to improve the visual quality of an image, especial night image. Night images' quality is poor because of under exposure, lack of even brightness distributing, and with more red pixels. In this paper, a Retinex algorithm is proposed to enhance the night image. First of all, the image is enhanced with the Retinex theory in two different groups parameters, according the analysis on the characteristics of the image. And then the enhanced image is fusion together according the discrete wavelet transform fusion algorithm. The simulation results show that the enhanced image is with greater brightness contrast and more clear edge, and the image visual effects has been improved significantly.