无线传感器网络中,移动节点使用的移动模型不同必然对节点运动轨迹产生巨大的影响。通过分析当前常用的三种移动模型,并对使用最广泛的Random Waypoint移动模型进行了仿真,根据该模型存在的问题,提出了一种新的移动模型:CRW移动模型:(co...无线传感器网络中,移动节点使用的移动模型不同必然对节点运动轨迹产生巨大的影响。通过分析当前常用的三种移动模型,并对使用最广泛的Random Waypoint移动模型进行了仿真,根据该模型存在的问题,提出了一种新的移动模型:CRW移动模型:(continuous random walk mobility model)。该模型使用牛顿力学原理来产生运动轨迹,节点的运动轨迹基本可以反映真实条件下节点的随机运动状况。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
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
文摘无线传感器网络中,移动节点使用的移动模型不同必然对节点运动轨迹产生巨大的影响。通过分析当前常用的三种移动模型,并对使用最广泛的Random Waypoint移动模型进行了仿真,根据该模型存在的问题,提出了一种新的移动模型:CRW移动模型:(continuous random walk mobility model)。该模型使用牛顿力学原理来产生运动轨迹,节点的运动轨迹基本可以反映真实条件下节点的随机运动状况。
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。
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.
