Tan Chang-geng;Xu Ke;Wang Jian-xin*;Chen Song-qiao
Journal of Central South University of Technology,2009年(2):265-268 ISSN：1005-9784
[Xu Ke; Tan Chang-geng; Wang Jian-xin; Chen Song-qiao] Cent S Univ, Sch Informat Sci & Engn, Changsha 410083, Peoples R China.
[Wang Jian-xin] Cent S Univ, Sch Informat Sci & Engn, Changsha 410083, Peoples R China.
wireless sensor network;network lifetime;moving scheme;residual energy
In the application of periodic data-gathering in sensor networks, sensor nodes located near the sink have to forward the data received from all other nodes to the sink, which depletes their energy very quickly. A moving scheme for the sink based on local residual energy was proposed. In the scheme, the sink periodically moves to a new location with the highest stay-value defined by the average residual energy and the number of neighbors. The scheme can balance energy consumption and prevent nodes around sink from draining their energy very quickly in the networks. The simulation results show that the scheme can prolong the network lifetime by 26%–65% compared with the earlier schemes where the sink is static or moves randomly.
Accompany with the understanding for geometry structure of manifold, more and more people used the Grassmannian manifold to face recognition via image sets. In order to improve the accuracy of recognition, several studies applied the discriminant analysis on such manifolds. However, most of these methods suffer from not considering the local structure of the manifold data. Accounting for success of the Symmetric Positive Definite (SPD) matrices in many algorithms, an improved method of discriminant analysis on Grassmannian manifold has been proposed in this paper. Similar to the conventional method, our approach map the SPD matrices to a high dimensional Hilbert space where Euclidean geometry applies also. With the Grassmannian kernel function which derived from Gaussian kernel use the different metric for Riemannian manifold of SPD matrices, the local geometry of mapping can be considered. The graph embedding on new feature space can get a better performance than conventional methods. Experiments on CMU PIE and BANCA databases demonstrate the efficient of our method.
局部切空间排列算法(Local Tangent Space Alignment)是一种具有严格数学推理的流形学习算法,能有效地学习出高维数据的低维嵌入坐标,但也存在一些不足,如对近邻点的选取依赖性较强、不适应处理高曲率分布、稀疏分布数据源.针对这些缺点,提出了一种基于几何距离摄动的局部切空间排列算法.利用几何摄动条件把样本空间划分为一组线性分块的组合,在每一个线性块上应用LTSA算法完成降维.实验结果表明了该算法的有效性.
在移动自组网中，减少移动节点电池能量消耗，延长网络总的使用时间，成为路由协议性能优劣的一个很重要的指标。本文介绍了一种关于节点能量估价PCF（Power Cost Function）的计算方法，它动态地反映了节点能量的剩余和使用情况，能够成功找到一条路径上的能量瓶颈节点。并且基于移动预测思想，综合考虑路径的总能耗最小和能量瓶颈节点的PCF两种情况，提出了基于链路稳定的能量有效AODV路由协议E-AODV（An Energy—efficient AODV routing protocol based on link stability）。模拟结果表明E-AODV协议比AODV路由协议具有更好的能量效率，它延长了节点的生存时间，提高了数据包的到达率。