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Application-aware in-network service and data fusion frameworks for distributed adaptive sensing systems.

机译：分布式自适应传感系统的应用感知网络内服务和数据融合框架。

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摘要

Distributed Collaborative Adaptive Sensing (DCAS) systems are emerging for applications, such as detection and prediction of hazardous weather using a network of radars. Collaborative Adaptive Sensing of the Atmosphere (CASA) is an example of these emerging DCAS systems. CASA is based on a dense network of weather radars that operate collaboratively to detect tornadoes and other hazardous atmospheric conditions. This dissertation presents an application- aware data transport framework to meet the data distribution/processing requirements of such mission-critical sensor applications over best-effort networks. Our application-aware data transport framework consists of overlay architecture and a programming interface. The architecture enables deploying application-aware in-network services in an overlay network to allow applications to best adapt to the network conditions. The programming interface facilitates development of applications within the architectural framework. We demonstrate the efficacy of the proposed framework by considering a DCAS application. We evaluate the proposed schemes in a network emulation environment and on Planetlab, a world-wide Internet test-bed. The proposed schemes are very effective in delivering high quality data to the multiple end users under various network conditions.;This dissertation also presents the design and implementation of an architectural framework for timely and accurate processing of radar data fusion algorithms. The preliminary version of the framework is used for real-time implementation of a multi-radar data fusion algorithm, the CASA network-based reflectivity retrieval algorithm. As a part of this research, a peer-to-peer (P2P) collaboration framework for multi-sensor data fusion is presented. Simulation-based results illustrate the effectiveness of the proposed P2P framework.;As multi-sensor fusion applications have a stringent real-time constraint, estimation of network delay across the sensor networks is important, particularly as they affect the quality of sensor fusion applications. We develop an analytical model for multi-sensor data fusion latency for the Internet-based sensor applications. Time scale-invariant burstiness observed across the network produces excessive network latencies. The analytical model considers the network delay due to the self-similar cross-traffic and latency for data synchronization for data fusion. A comparison of the analytical model and simulation-based results show that our model provides a good estimation for the multi-sensor data fusion latency.
机译：分布式协作自适应传感（DCAS）系统正在出现，其应用包括使用雷达网络检测和预测危险天气。这些新兴的DCAS系统的一个示例是大气协同自适应感（CASA）。 CASA基于密集的天气雷达网络，这些网络协同工作以检测龙卷风和其他危险的大气状况。本文提出了一种应用程序感知的数据传输框架，以满足在尽力而为网络上此类任务关键型传感器应用程序的数据分发/处理要求。我们的应用程序感知数据传输框架由覆盖体系结构和编程接口组成。该体系结构允许在覆盖网络中部署应用程序感知的网络内服务，以使应用程序最佳地适应网络条件。编程接口促进了体系结构框架内应用程序的开发。通过考虑DCAS应用，我们证明了所提出框架的有效性。我们在网络仿真环境和全球互联网测试平台Planetlab上评估提出的方案。所提出的方案对于在各种网络条件下向多个终端用户提供高质量数据非常有效。;本文还提出了一种及时，准确地处理雷达数据融合算法的架构框架的设计与实现。该框架的初步版本用于实时实施多雷达数据融合算法，即基于CASA网络的反射率检索算法。作为这项研究的一部分，提出了一种用于多传感器数据融合的对等（P2P）协作框架。基于仿真的结果说明了所提出的P2P框架的有效性。由于多传感器融合应用程序具有严格的实时约束，因此估计整个传感器网络的网络延迟非常重要，尤其是因为它们会影响传感器融合应用程序的质量。我们为基于Internet的传感器应用程序开发了用于多传感器数据融合延迟的分析模型。在整个网络中观察到的时标不变突发性会产生过多的网络延迟。分析模型考虑了由于自相似交叉流量和网络数据融合延迟所引起的网络延迟。分析模型和基于仿真的结果的比较表明，我们的模型为多传感器数据融合等待时间提供了很好的估计。

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  • 作者

    Lee, Pan Ho.;

  • 作者单位

    Colorado State University.;

  • 授予单位 Colorado State University.;
  • 学科 Engineering Electronics and Electrical.
  • 学位 Ph.D.
  • 年度 2009
  • 页码 169 p.
  • 总页数 169
  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类 无线电电子学、电信技术;
  • 关键词

  • 入库时间 2022-08-17 11:38:26

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