EAGER: Collaborative Research: Network Inference and Data Collection Based on Compressed Sensing in Large-Scale Wireless Sensor Networking

EAGER：协作研究：大规模无线传感器网络中基于压缩感知的网络推理和数据收集

• 批准号: 1251995
• 负责人: Xu Liang
• 金额: $ 3.59万
• 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: 2012
• 资助国家: 美国
• 起止时间: 2012-09-15 至 2014-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1251995&HistoricalAwards=false
• 关键词: EAGER; Collaborative; Research; Network; Inference

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 and, hence, open new exciting opportunities for numerous scientific endeavors. Since sensor nodes are unattended and battery-powered, network monitoring/inference 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 proposal is devoted to a fundamental investigation of energy-efficient network monitoring/inference and data collections in large-scale WSNs, based on the recent breakthrough of compressed sensing (CS) through an integrated theoretical and empirical approach. The research plan aims to develop a novel and rigorous framework of topology inference for real-world WSNs operated in highly noisy communication environments. This proposed exploratory research is different from current approaches, which may create a new paradigm of optimal design, development, and management/operations for large-scale WSNs to significantly extend their lifetime. This, in turn, would lead to a substantial reduction of the current prohibitive cost of WSN deployment, and thus could be high-reward for the deployment of large-scale monitoring WSNs for scientific, civic, national security, and military purposes in the near future. The proposed education plan creates a new interdisciplinary educational practice for both undergraduate and graduate students through hands-on experience with an extended WSN testbed. The outreach includes summer camps for school students using an extended 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，因此为许多科学工作开辟了新的令人兴奋的机会。由于传感器节点是无人值守和电池供电的，网络监测/间接测量在汇（S）和节能的推理是至关重要的部署大规模的环境无线传感器网络。因此，一个可行的节能网络监控和数据收集框架对于显著改善WSN管理/操作并降低其部署成本至关重要。该提案致力于大规模无线传感器网络中的节能网络监测/推理和数据收集的基本调查，基于压缩传感（CS）的最新突破，通过综合的理论和实证方法。该研究计划旨在为在高噪声通信环境中运行的真实世界的无线传感器网络开发一种新颖而严格的拓扑推理框架。这一探索性研究不同于目前的方法，这可能会为大规模无线传感器网络的优化设计、开发和管理/运营创造一个新的范例，以显着延长其寿命。反过来，这将导致大幅降低目前的无线传感器网络部署的高昂成本，因此可能是高回报的科学，公民，国家安全和军事目的的大规模监测无线传感器网络的部署在不久的将来。拟议的教育计划创建了一个新的跨学科的教育实践，为本科生和研究生通过动手经验与扩展的无线传感器网络测试床。推广活动包括为学校学生举办夏令营，使用扩展的无线传感器网络测试平台。