...
  • 主题
高级搜索  >
历史搜索 清空历史
首页> 外文期刊>IEEE Wireless Communications >Apply geometric duality to energy-efficient non-local phenomenon awareness using sensor networks
【24h】

Apply geometric duality to energy-efficient non-local phenomenon awareness using sensor networks

机译：使用传感器网络将几何对偶应用于高能效的非局部现象感知

获取原文
获取原文并翻译 | 示例
   

获取外文期刊封面封底 >>

       
页面导航
  • 摘要
  • 著录项
  • 相似文献
  • 相关主题

摘要

A powerful concept to cope with resource limitations and information redundancy in wireless sensor networks is the use of collaboration groups to distill information within the network and suppress unnecessary activities. When the phenomena to be monitored have large geographical extents, it is not obvious how to define these collaboration groups. This article presents the application of geometric duality to form such groups for sensor selection and non-local phenomena tracking. Using a dual-space transformation, which maps a non-local phenomenon (e.g., the edge of a half-plane shadow) to a single point in the dual space and maps locations of distributed sensor nodes to a set of lines that partitions the dual space, one can turn off the majority of the sensors to achieve resource preservation without losing detection and tracking accuracy. Since the group so defined may consist of nodes that are far away in physical space, we propose a hierarchical architecture that uses a small number of computationally powerful nodes and a massive number of power constrained motes. By taking advantage of the continuity of physical phenomena and the duality principle, we can greatly reduce the power consumption in non-local phenomena tracking and extend the lifetime of the network.
机译：应对无线传感器网络中的资源限制和信息冗余的强大概念是使用协作组在网络中提取信息并抑制不必要的活动。当要监视的现象具有较大的地理范围时，如何定义这些协作组并不明显。本文介绍了几何对偶的应用，以形成用于传感器选择和非局部现象跟踪的此类组。使用对偶空间变换，该变换将非局部现象（例如，半平面阴影的边缘）映射到对偶空间中的单个点，并将分布式传感器节点的位置映射到对对偶空间进行分区的一组线这样一来，您就可以关闭大多数传感器，以实现资源保护，而不会丢失检测和跟踪精度。由于这样定义的组可能由物理空间中距离较远的节点组成，因此我们提出了一种层次结构，该结构使用少量的计算功能强大的节点和大量受功率限制的节点。利用物理现象的连续性和对偶原理，可以大大降低非局部现象跟踪的功耗，延长网络的使用寿命。

著录项

相似文献

  • 外文文献
  • 中文文献
  • 专利
获取原文

客服邮箱：kefu@zhangqiaokeyan.com

京公网安备：11010802029741号 ICP备案号：京ICP备15016152号-6 六维联合信息科技 (北京) 有限公司©版权所有

  • 客服微信

  • 服务号