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: 1319331
• 负责人: Xu Liang
• 金额: $ 23.25万
• 依托单位: University of Pittsburgh
• 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=1319331&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一组令人难以置信的丰富的观察模式。无线传感器网络（WSNs）的最新进展使得能够在前所未有的高空间密度和长时间持续时间下连续监测各种物理现象，从而为许多科学工作开辟了令人兴奋的新机会。由于传感器节点是无人值守和电池供电，网络监测/断层扫描间接测量在汇（S）和节能是至关重要的部署大规模的环境无线传感器网络。因此，一个可行的框架，节能的网络监测和数据收集是从根本上重要的是显着提高无线传感器网络的管理/操作，并降低其部署costs.This项目研究节能的网络监测/断层扫描和数据收集在大规模的室外无线传感器网络，最近的突破压缩传感（CS）的基础上，通过综合的理论和实证方法。本计画主要研究无线网路在动态环境下，因信道衰减及干扰所造成的动态路由问题。这个项目的目标是开发一个新的和严格的拓扑层析成像的框架，为现实世界的无线传感器网络在高噪声的通信环境中运行。动态路由拓扑恢复算法被设计用于在信宿处接收的完全间接测量和不完全间接测量。层析成像方法的准确性进行了研究分析和经验。所开发的WSN拓扑层析成像框架不仅对WSN的路由改进、拓扑控制、热点消除和异常检测具有重要意义，而且对新兴的基于CS的数据收集也具有重要意义。该方法扩展了现有的CS技术，形成了一个统一的框架，在大规模的无线传感器网络的网络断层扫描和数据收集，能源效率的无线传感器网络拓扑断层扫描和数据收集协议套件的开发。开发的框架和协议套件将在一个真实世界的环境中的无线传感器网络测试床在丘陵watering.The项目的目的是创建一个新的范式的优化设计，开发和管理/操作的大规模无线传感器网络，以显着延长其寿命进行验证和评估。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WSN部署的高昂成本。该项目通过实际WSN测试平台的实践经验，为本科生和研究生创建了一个跨学科的教育实践。推广活动包括夏令营和使用无线传感器网络测试平台的学生科学项目。