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令人难以置信的丰富观察方式。无线传感器网络（WSN）的最新进展使得能够在前所未有的高空间密度和长时间的持续时间对各种物理现象进行持续监测，从而为众多科学努力打开了令人兴奋的新机会。由于传感器节点是无人看管的，并且是击球式的，因此从水槽中的间接测量和节能的网络监控/断层扫描对于部署大规模环境WSN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。该项目根据最近通过集成的方法和集成的方法调查了大型室外WSN中的节能网络监控/层析成绩和数据收集。由于通道褪色和干扰，项目研究WSN拓扑层析成像，用于在无线链路动态下进行动态路由。该项目的目标是为在高度嘈杂的通信环境中运行的现实世界中的WSN开发一个新颖而严格的拓扑层析成像框架。为完整的间接测量和不完整的间接测量设计，设计了动态路由拓扑恢复算法。在分析和经验上研究了断层扫描方法的准确性。开发的WSN拓扑断层扫描框架不仅对于WSN的路由改进，拓扑控制，热点消除和实践中的异常检测至关重要，而且对于基于CS的新兴数据收集也至关重要。这种方法扩展了当前的CS技术，以在大规模WSN中形成网络断层扫描和数据收集的统一框架，并开发了节能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现实环境WSN测试中进行验证和评估。该项目旨在为大型WSN的最佳设计，开发和管理/操作创建新的范式，以显着延长其生命周期。这将大大降低大规模WSN部署在不久的将来，用于科学，公民，国家安全和军事目的的过失成本。该项目通过现实世界中的WSN测试床为本科和研究生创造了跨学科的教育实践。外展活动包括使用WSN测试台为学校学生提供的夏令营和科学项目。