This paper proposes an adaptive localization approach for wireless sensor networks based on Gauss-Markov mobility model. In the approach,the perpendicular bisector strategy,the virtual repulsive strategy,and the veloc...This paper proposes an adaptive localization approach for wireless sensor networks based on Gauss-Markov mobility model. In the approach,the perpendicular bisector strategy,the virtual repulsive strategy,and the velocity adjustment strategy are properly combined to enhance localization effciency. The velocity adjustment strategy causes that the mobile anchor node automatically tunes its velocity. The perpendicular bisector strategy locally adjusts trajectory for the mobile anchor node,which ensures that unknown nodes obtain enough non-collinear anchor coordinates as soon as possible. The virtual repulsive strategy impels that the mobile anchor node rapidly leaves the communication range of location-aware nodes or returns to the surveillance region after the mobile anchor node was out of the boundary. Both theoretical analysis and simulation studies show that this approach can increase localization accuracy,consume less energy,and cover more surveillance region during the same period than virtual beacons-energy ratios localization scheme using the Gauss-Markov mobility model.展开更多
针对以往移动无线传感器网络研究只是单纯地对移动群体进行分簇而没有充分利用组群移动的内部稳定性的问题,结合组移动模型中节点运动的规律和内聚性原理,采用平滑高斯半马尔可夫移动模型刻画组内单个节点移动特征,构建了一种适合移动...针对以往移动无线传感器网络研究只是单纯地对移动群体进行分簇而没有充分利用组群移动的内部稳定性的问题,结合组移动模型中节点运动的规律和内聚性原理,采用平滑高斯半马尔可夫移动模型刻画组内单个节点移动特征,构建了一种适合移动网络的稳定生成树算法(GM-base stable spanning tree algorithm,GSST);实验证明,该算法从单个节点运动变化入手,在预测未来节点运动情况,选择稳定的链路构建网络结构方面,提高了移动网络的稳定性;同时,利用树的分层特征,简化移动网络的组网过程,并实现网络重组局部化;该算法有效延长节点存活率,均衡数据传输量。展开更多
该文研究了无监督遥感图像分类问题。文中构造了图像的隐马尔可夫随机场模型(HiddenMarkov Random Fleid,HMRF),并且提出了基于该模型的图像分类算法。该文采用有限高斯混合模型(Finite Gaussian Mixture,FGM)描述图像像素灰度的条件概...该文研究了无监督遥感图像分类问题。文中构造了图像的隐马尔可夫随机场模型(HiddenMarkov Random Fleid,HMRF),并且提出了基于该模型的图像分类算法。该文采用有限高斯混合模型(Finite Gaussian Mixture,FGM)描述图像像素灰度的条件概率分布,使用EM(Expectation-Maximization)算法解决从不完整数据中估计概率模型参数问题。针对遥感图像分布的不均匀特性,该文提出的算法没有采用固定的马尔可夫随机场模型参数,而是在递归分类算法中分级地调整模型参数以适应区域的变化。实验结果表明了该文算法的有效性,分类算法处理精度高于C-Means聚类算法.。展开更多
基金Supported by National Natural Science Foundation of China(60776834, 60870010)
文摘This paper proposes an adaptive localization approach for wireless sensor networks based on Gauss-Markov mobility model. In the approach,the perpendicular bisector strategy,the virtual repulsive strategy,and the velocity adjustment strategy are properly combined to enhance localization effciency. The velocity adjustment strategy causes that the mobile anchor node automatically tunes its velocity. The perpendicular bisector strategy locally adjusts trajectory for the mobile anchor node,which ensures that unknown nodes obtain enough non-collinear anchor coordinates as soon as possible. The virtual repulsive strategy impels that the mobile anchor node rapidly leaves the communication range of location-aware nodes or returns to the surveillance region after the mobile anchor node was out of the boundary. Both theoretical analysis and simulation studies show that this approach can increase localization accuracy,consume less energy,and cover more surveillance region during the same period than virtual beacons-energy ratios localization scheme using the Gauss-Markov mobility model.
文摘针对以往移动无线传感器网络研究只是单纯地对移动群体进行分簇而没有充分利用组群移动的内部稳定性的问题,结合组移动模型中节点运动的规律和内聚性原理,采用平滑高斯半马尔可夫移动模型刻画组内单个节点移动特征,构建了一种适合移动网络的稳定生成树算法(GM-base stable spanning tree algorithm,GSST);实验证明,该算法从单个节点运动变化入手,在预测未来节点运动情况,选择稳定的链路构建网络结构方面,提高了移动网络的稳定性;同时,利用树的分层特征,简化移动网络的组网过程,并实现网络重组局部化;该算法有效延长节点存活率,均衡数据传输量。
文摘该文研究了无监督遥感图像分类问题。文中构造了图像的隐马尔可夫随机场模型(HiddenMarkov Random Fleid,HMRF),并且提出了基于该模型的图像分类算法。该文采用有限高斯混合模型(Finite Gaussian Mixture,FGM)描述图像像素灰度的条件概率分布,使用EM(Expectation-Maximization)算法解决从不完整数据中估计概率模型参数问题。针对遥感图像分布的不均匀特性,该文提出的算法没有采用固定的马尔可夫随机场模型参数,而是在递归分类算法中分级地调整模型参数以适应区域的变化。实验结果表明了该文算法的有效性,分类算法处理精度高于C-Means聚类算法.。
