• 主题
高级搜索  >
历史搜索 清空历史
首页> 外文期刊>IEEE sensors journal >Energy Efficient Data Acquisition Techniques Using Context Aware Sensing for Landslide Monitoring Systems
【24h】

Energy Efficient Data Acquisition Techniques Using Context Aware Sensing for Landslide Monitoring Systems

机译：基于上下文感知的滑坡监测系统高效节能数据采集技术

获取原文
获取原文并翻译 | 示例
           
页面导航
  • 摘要
  • 著录项
  • 相似文献
  • 相关主题

摘要

Real-time wireless sensor networks are an emerging technology for continuous environmental monitoring. But real-world deployments are constrained by resources, such as power, memory, and processing capabilities. In this paper, we discuss a set of techniques to maximize the lifetime of a system deployed in south India for detecting rain-fall induced landslides. In this system, the sensing subsystem consumes 77.5%, the communication subsystem consumes 22%, and the processing subsystem consumes 0.45% of total power consumption. Hence, to maximize the lifetime of the system, the sensing subsystem power consumption has to be reduced. The major challenge to address is the development of techniques that reduce the power consumption, while preserving the reliability of data collection and decision support by the system. This paper proposes a wavelet-based sampling algorithm for choosing the minimum sampling rate for ensuring the data reliability. The results from the wavelet sampling algorithm along with the domain knowledge have been used to develop context aware data collection models that enhance the lifetime of the system. Two such models named context aware data management (CAD) and context aware energy management (CAE) have been devised. The results show that the CAD model extends the lifetime by six times and the CAE model does so by 20 times when compared with the continuous data collection model, which is the existing approach. In this paper, we also developed mathematical modeling for CAD and CAE, which have been validated using real-time data collected in the past.
机译：实时无线传感器网络是一种用于持续环境监控的新兴技术。但是现实世界中的部署受到资源（例如电源，内存和处理能力）的限制。在本文中，我们讨论了一套技术，以最大化在印度南部部署的用于检测降雨引起的滑坡的系统的使用寿命。在该系统中，传感子系统消耗了77.5％，通信子系统消耗了22％，处理子系统消耗了总功耗的0.45％。因此，为了最大化系统的寿命，必须降低感测子系统的功耗。解决的主要挑战是在降低功耗的同时，保持系统数据收集和决策支持的可靠性的技术发展。提出了一种基于小波的采样算法，用于选择最小采样率以保证数据的可靠性。小波采样算法的结果以及领域知识已被用于开发上下文感知的数据收集模型，从而延长了系统的使用寿命。已经设计了两个这样的模型，分别称为情境感知数据管理（CAD）和情境感知能源管理（CAE）。结果表明，与现有的连续数据收集模型相比，CAD模型可以将寿命延长六倍，而CAE模型可以将寿命延长20倍。在本文中，我们还开发了CAD和CAE的数学模型，这些模型已经使用过去收集的实时数据进行了验证。

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