CAREER: PROSE: Providing Robustness in Systems of Embedded Sensors

职业:PROSE:为嵌入式传感器系统提供鲁棒性

基本信息

  • 批准号:
    0546072
  • 负责人:
  • 金额:
    $ 44.97万
  • 依托单位:
  • 依托单位国家:
    美国
  • 项目类别:
    Continuing Grant
  • 财政年份:
    2006
  • 资助国家:
    美国
  • 起止时间:
    2006-07-01 至 2012-06-30
  • 项目状态:
    已结题

项目摘要

While sensor applications promise profound scientific and economic impacts on our society, their success is nonetheless determined by whether the sensor networks can provide a steady stream of correct data. This need for continuous data provisioning over a significant time period, however, imposes great challenges to the underlying system. Specifically, wireless sensor networks are prone to a large array of hazards, such as frequent node failures, congestion, and sensing errors. These challenges are further complicated by the fact that sensor systems are usually seriously energy constrained.This project has three main components: DADA, TARA, and MARA. DADA, a 2-Dimensional Adaptive Scheduling Framework, rapidly repairs network coverage and connectivity by cleverly waking up the redundant nodes that are needed for repairing holes created by the node failure. TARA, a Topology-Aware Resource Adaptation Framework, strives to increase the resource provisioning by bringing more sensor nodes online to accommodate those packets that contain valuable data, potentially regarding the source of the congestion. MARA, a Measurement Assurance and Robust Aggregation Framework, provides a set of data classification and cleansing tools that validate the data before they flow into the network. This project attempts to deliver the guarantee that sensor systems must gracefully recover themselves in the presence of network exceptions, in order to satisfy the application needs as well as lower the network management costs. In addition to technical papers that report the research results, this project will also produce a suite of software tools that will be made available to the community.
虽然传感器应用有望对我们的社会产生深远的科学和经济影响,但它们的成功取决于传感器网络是否能够提供稳定的正确数据流。然而,这种在很长一段时间内持续提供数据的需求给底层系统带来了巨大的挑战。具体地说,无线传感器网络容易出现大量的危险,例如频繁的节点故障、拥塞和感知错误。由于传感器系统通常受到严重的能源限制,这些挑战变得更加复杂。该项目有三个主要组成部分:达达、塔拉和玛拉。DADA是一种二维自适应调度框架,通过巧妙地唤醒修复节点故障造成的漏洞所需的冗余节点,快速修复网络覆盖和连通性。TARA是一种拓扑感知的资源适配框架,它通过使更多的传感器节点在线来容纳那些包含有价值的数据的分组,从而努力增加资源供应,这些分组可能涉及拥塞的来源。MARA是一种测量保证和健壮聚合框架,它提供了一套数据分类和清理工具,可在数据流入网络之前对其进行验证。本项目试图提供传感器系统在出现网络异常时能够正常恢复的保证,以便在满足应用需求的同时降低网络管理成本。除了报告研究结果的技术论文外,该项目还将生成一套软件工具,供社区使用。

项目成果

期刊论文数量(0)
专著数量(0)
科研奖励数量(0)
会议论文数量(0)
专利数量(0)

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Yanyong Zhang其他文献

BiFocus: using radio-optical beacons for an augmented reality search application
BiFocus:使用无线电光学信标进行增强现实搜索应用
  • DOI:
    10.1145/2462456.2465706
  • 发表时间:
    2013
  • 期刊:
  • 影响因子:
    4.9
  • 作者:
    A. Ashok;Chenren Xu;Tam N. Vu;M. Gruteser;R. Howard;Yanyong Zhang;N. Mandayam;Wenjia Yuan;Kristin J. Dana
  • 通讯作者:
    Kristin J. Dana
LDP: A Local Diffusion Planner for Efficient Robot Navigation and Collision Avoidance
LDP:用于高效机器人导航和避免碰撞的局部扩散规划器
  • DOI:
  • 发表时间:
    2024
  • 期刊:
  • 影响因子:
    0
  • 作者:
    Wenhao Yu;Jie Peng;Huanyu Yang;Junrui Zhang;Yifan Duan;Jianmin Ji;Yanyong Zhang
  • 通讯作者:
    Yanyong Zhang
A Model of Passive Human Motion Recognition Using Two-Layer Wireless Links
使用两层无线链路的被动人体运动识别模型
On the Cache-and-Forward Network Architecture
浅谈缓存转发网络架构
The Boomerang Protocol: Tieing Data to Geographic Locations in Mobile Disconnected Networks
Boomerang 协议:将数据与移动断开网络中的地理位置联系起来

Yanyong Zhang的其他文献

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{{ truncateString('Yanyong Zhang', 18)}}的其他基金

NeTS: Small: Transmit Only: Green Communication for Dense Wireless Systems
NeTS:小型:仅传输:密集无线系统的绿色通信
  • 批准号:
    1423020
  • 财政年份:
    2014
  • 资助金额:
    $ 44.97万
  • 项目类别:
    Standard Grant
CT - ISG: ROME: Robust Measurement in Sensor Networks
CT - ISG:ROME:传感器网络中的稳健测量
  • 批准号:
    0831186
  • 财政年份:
    2008
  • 资助金额:
    $ 44.97万
  • 项目类别:
    Standard Grant
Collaborative Research: CSR-SMA+AES: Pro-Active Runtime Health Enhancement of Large-Scale Parallel Systems Using PROGNOSIS
合作研究:CSR-SMA AES:使用 PROGNOSIS 主动增强大规模并行系统的运行时健康状况
  • 批准号:
    0614976
  • 财政年份:
    2006
  • 资助金额:
    $ 44.97万
  • 项目类别:
    Continuing Grant
Collaborative Research: CSR---SMA+AES: PROGNOSIS to Enhance the Runtime Health of Large Scale Parallel Systems
合作研究:CSR---SMA AES:增强大规模并行系统运行时健康状况的预测
  • 批准号:
    0509164
  • 财政年份:
    2005
  • 资助金额:
    $ 44.97万
  • 项目类别:
    Standard Grant

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