摘要
定位技术是无线传感器网络中关键的基础支撑技术,目前提出了许多静态网络的节点定位算法,移动无线传感器网络的定位研究相对较少.针对定位节点和参考节点随机运动的网络模型,提出了一个基于动态网格划分的蒙特卡罗定位算法.算法中当接收的参考节点数超过一定阈值时使用最远距离节点选择模型,选出部分参考节点参与定位和信息转发,节约能耗.接着基于选择的或所有接收的参考节点构建采样区域,进行网格划分,使用网格单元数计算最大采样次数,在采样区域内采样并使用误差补偿的运动模型进行过滤,提高了采样效率,减少了计算开销,并保证了较好的定位精度.仿真实验表明算法在定位精度,计算开销、能耗等方面都具有较好的性能.
Localization is extremely critical for many applications in wireless sensor networks. Without the location of sensor nodes, collected information is valueless. Meanwhile, location information is also helpful for many network operations such as clustering, topology control, and geographical routing. Localization is an extensively studied problem in wireless sensor networks. Some localization algorithms for static wireless sensor networks have been proposed. However, little study has been done about the localization in mobile wireless sensor networks. A Monte-Carlo localization algorithm is presented based on dynamic grid division for wireless sensor networks, in which the nodes can move randomly. In the presented algorithm, when the number of received one- hop anchors is lager than a threshold, a farthest distance selecting algorithm is used. Only these selected anchors take part in localization and data transmitting, and they can conserve some energy. Then sampling area is created based on selected or all received anchor information, a grid division is made, and the maximum sampling number is computed with cell counts. Next, sampling is made in the created area and filtering is done with a mobility model of error compensation, which can improve the sampling efficiency and reduce the computing overhead. The simulation demonstrates that the proposed algorithm provides better performance in localization precision, computing overhead, and energy consumption.
出处
《计算机研究与发展》
EI
CSCD
北大核心
2008年第11期1920-1927,共8页
Journal of Computer Research and Development
基金
国家自然科学基金项目(60673061)
教育部高等学校博士学科点专项科研基金项目(20060532024)~~
关键词
无线传感器网络
定位
蒙特卡罗
网格
采样
wireless sensor network
localization
Monte-Carlo
grid
sampling
