• 主题
高级搜索  >
历史搜索 清空历史
首页> 外文会议>ACM/IEEE international conference on information processing in sensor networks >Online Distributed Sensor Selection
【24h】

Online Distributed Sensor Selection

机译：在线分布式传感器选择

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

摘要

A key problem in sensor networks is to decide which sensors to query when, in order to obtain the most useful information (e.g., for performing accurate prediction), subject to constraints (e.g., on power and bandwidth). In many applications the utility function is not known a priori, must be learned from data, and can even change over time. Furthermore for large sensor networks solving a centralized optimization problem to select sensors is not feasible, and thus we seek a fully distributed solution. In this paper, we present Distributed Online Greedy (DOG), an efficient, distributed algorithm for repeatedly selecting sensors online, only receiving feedback about the utility of the selected sensors. We prove very strong theoretical no-regret guarantees that apply whenever the (unknown) utility function satisfies a natural diminishing returns property called submodularity. Our algorithm has extremely low communication requirements, and scales well to large sensor deployments. We extend DOG to allow observation-dependent sensor selection. We empirically demonstrate the effectiveness of our algorithm on several real-world sensing tasks.
机译：在传感器网络中的一个关键问题是哪些传感器决定查询时，为了获得最有用的信息（例如，用于执行精确的预测），受约束（例如，功率和带宽）。在许多应用中的效用函数是事先不知道，必须从数据中了解到，甚至可以改过来的时间。此外，对于大型传感器网络解决一个集中式的优化问题，选择的传感器是不可行的，因此，我们寻求一个完全分布式的解决方案。在本文中，我们提出的分布式在线贪婪（DOG），一种有效的，分布式算法反复选择的传感器的在线，仅在接收到关于所选择的传感器的效用反馈。我们证明申请很强的理论无悔保证每当（未知），效用函数满足称为子模的自然报酬递减特性。我们的算法具有极低的通信需求，并很好地扩展到大尺寸传感器的部署。我们向狗允许观察相关的传感器选择。我们经验证明我们的算法的几个现实世界的感知任务的效能。

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