NeTS: Small: Collaborative Research: Compressed Network Tomography and Data Collection in Large-Scale Wireless Sensor Networking

NeTS：小型：协作研究：大规模无线传感器网络中的压缩网络断层扫描和数据收集

• 批准号: 1320132
• 负责人: Yao Liang
• 金额: $ 23.31万
• 依托单位: Indiana University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2013
• 资助国家: 美国
• 起止时间: 2013-10-01 至 2018-09-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1320132&HistoricalAwards=false
• 关键词: NeTS; Small; Collaborative; Research; Compressed

Our physical world presents an incredibly rich set of observation modalities. Recent advances in wireless sensor networks (WSNs) enable the continuous monitoring of various physical phenomena at unprecedented high spatial densities and long time durations, hence opening exciting new opportunities for numerous scientific endeavors. Since sensor nodes are unattended and batterypowered, network monitoring/tomography from indirect measurements at the sink(s) and energy conservation are critical in the deployment of large-scale environmental WSNs. Therefore, a viable framework for energy-efficient network monitoring and data collection is fundamentally important to significantly improve WSN management/operations and reduce its deployment costs.This project investigates the energy-efficient network monitoring/tomography and data collections in large-scale outdoor WSNs, based on the recent breakthrough of compressed sensing (CS) through an integrated theoretical and empirical approach. The project studies WSN topology tomography for dynamic routing under wireless link dynamics due to channel fading and interference. The objectives of this project are to develop a novel and rigorous framework of topology tomography for real-world WSNs operated in highly noisy communication environments. Dynamic routing topology recovery algorithms are devised for both complete indirect measurements and incomplete indirect measurements received at the sink(s). The accuracy of the tomography approach is studied both analytically and empirically. The developed WSN topology tomography framework can be essential not only for WSN's routing improvement, topology control, hot spot elimination, and anomaly detection in practice, but also for emerging CS-based data collection. This approach extends the current CS technology to form a unified framework for network tomography and data collection in large-scale WSNs, upon which energy-efficient WSN topology tomography and data gathering protocol suite is developed. The developed framework and protocol suite will be validated and evaluated in a real-world environmental WSN testbed in a hilly watershed.The project intends to create a new paradigm of optimal design, development, and management/operations for large-scale WSNs to significantly extend their lifetime. This would lead to a substantial reduction of the prohibitive cost of large-scale WSN deployments for scientific, civic, national security, and military purposes in the near future. The project creates an interdisciplinary educational practice for both undergraduate and graduate students through hands-on experience with a real-world WSN testbed. The outreach includes summer camps and scientific projects for school students using the WSN testbed.

我们的物理世界呈现出极其丰富的观察方式。无线传感器网络（WSN）的最新进展使得能够以前所未有的高空间密度和长时间持续监测各种物理现象，从而为众多科学事业带来令人兴奋的新机遇。由于传感器节点是无人值守且由电池供电的，因此在接收器处间接测量的网络监控/断层扫描和节能对于大规模环境 WSN 的部署至关重要。因此，一个可行的节能网络监控和数据收集框架对于显着改善 WSN 管理/运营并降低其部署成本至关重要。该项目研究大规模室外 WSN 中的节能网络监控/层析成像和数据收集，基于压缩感知（CS）最近通过综合理论和经验方法取得的突破。该项目研究了由于信道衰落和干扰而导致的无线链路动态下动态路由的 WSN 拓扑层析成像。该项目的目标是为在高噪声通信环境中运行的现实世界无线传感器网络开发一种新颖且严格的拓扑断层扫描框架。动态路由拓扑恢复算法被设计用于在宿处接收到的完整间接测量和不完整间接测量。对断层扫描方法的准确性进行了分析和实证研究。所开发的WSN拓扑层析成像框架不仅对于实践中WSN的路由改进、拓扑控制、热点消除和异常检测至关重要，而且对于新兴的基于CS的数据收集也至关重要。该方法扩展了当前的CS技术，形成了大规模WSN中网络层析成像和数据收集的统一框架，并在此基础上开发了节能的WSN拓扑层析成像和数据收集协议套件。开发的框架和协议套件将在丘陵流域的真实环境 WSN 测试台中进行验证和评估。该项目旨在为大规模 WSN 创建优化设计、开发和管理/运营的新范例，以显着扩展他们的一生。这将导致在不久的将来大幅降低用于科学、公民、国家安全和军事目的的大规模无线传感器网络部署的成本。该项目通过真实世界 WSN 测试平台的实践经验，为本科生和研究生创建跨学科的教育实践。外展活动包括为使用 WSN 测试平台的在校学生举办夏令营和科学项目。