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: 1252066
• 负责人: Yao Liang
• 金额: $ 6.41万
• 依托单位: Indiana University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 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=1252066&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. This 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 our extended WSN testbed. The outreach plan includes summer camps for school students using an extended WSN testbed.

我们的物质世界呈现了一套令人难以置信的丰富的观察模式。无线传感器网络(WSN)的最新进展使人们能够以前所未有的高空间密度和长时间连续监测各种物理现象，从而为许多科学努力打开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，因此网络监测/来自汇点间接测量的推断(S)和节能在大规模环境无线传感器网络的部署中至关重要。因此，一个可行的节能网络监控和数据收集框架对于显著改善无线传感器网络的管理/运营并降低其部署成本至关重要。该方案致力于大规模无线传感器网络中节能网络监测/推理和数据收集的基础性研究，基于压缩感知(CS)的最新突破，通过理论和经验相结合的方法。这一研究计划旨在为运行在高噪声通信环境中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现有的方法，它可能为大规模无线传感器网络的优化设计、开发和管理/运营创造一种新的范式，以显著延长其生命周期。这反过来将导致大幅降低目前令人望而却步的无线传感器网络部署成本，因此可能是在不久的将来为科学、民用、国家安全和军事目的部署大规模监测无线传感器网络的高额回报。拟议的教育计划通过我们扩展的WSN试验床的实践经验，为本科生和研究生创建了一种新的跨学科教育实践。外展计划包括使用扩展的WSN试验床为在校学生举办夏令营。