NeTS: Medium: Collaborative Research: Opportunistic and Compressive Sensing in Wireless Sensor Networks

NeTS：媒介：协作研究：无线传感器网络中的机会和压缩感知

基本信息

批准号：
0964713
负责人：
Qilian Liang
金额：
$ 41.5万
依托单位：
University of Texas at Arlington
依托单位国家：
美国
项目类别：
Continuing Grant
财政年份：
2010
资助国家：
美国
起止时间：
2010-04-01 至 2016-09-30
项目状态：
已结题

来源：
https://www.nsf.gov/awardsearch/showAward?AWD_ID=0964713&HistoricalAwards=false
关键词：
NeTS Medium Collaborative Research Opportunistic

项目摘要

This collaborative project investigates Opportunistic Sensing (OS) and Compressive Sensing (CS) in Wireless Sensor Networks (WSNs). OS refers to a paradigm in which a WSN can automatically discover and select sensor modalities and sensors based on an operational scenario, resulting in an adaptive network that automatically finds scenario-dependent, objective-driven opportunities with optimized performance. CS is a novel sensing/sampling paradigm that goes against the common wisdom in data acquisition. Both OS and CS help improve efficient operations and performance of WSNs significantly. In particular, OS aims at reduction from space by selecting a subset of sensors and modalities for efficient data fusion, whereas CS targets reduction in sampling by selecting a subset of samples non-uniformly. Therefore, theoretical foundations and algorithms for opportunistic and compressive sensing are essential for advancing the state of the art in WSNs that not only ensure effective utilization of sensing assets but also provide robust optimal performance. This project addresses fundamental research issues from information theoretic viewpoint to evaluate joint OS and CS distortions, develop OS and collaborative CS schemes for better performance of WSNs, and cross-layer design to adapt to the non-uniform sampling in CS-based WSNs.This project will make a significant contribution to the theory and applications of opportunistic and compressive sensing to WSNs and will have a broad and deep social impact in homeland security and defense. Under-represented and female students from different societies will be recruited, and seminars and in-house short courses will be offered to local industry via IEEE.

该协作项目调查了无线传感器网络（WSN）中的机会性传感（OS）和压缩传感（CS）。 OS是指WSN可以根据操作场景自动发现并选择传感器模式和传感器的范式，从而产生自适应网络，该网络自动发现方案依赖于方案，目标驱动的机会，并具有优化的性能。 CS是一种新颖的感测/采样范式，违背了数据采集中的共同智慧。 OS和CS都有助于显着提高WSN的有效操作和性能。特别是，OS的目的是通过选择传感器的子集和模态以进行有效数据融合来减少空间，而CS目标通过不均匀选择样品的子集来减少采样。因此，对于机会和压缩感知的理论基础和算法对于推进WSN中的最新状态至关重要，WSN不仅可以确保有效利用感应资产，而且还提供了强大的最佳性能。该项目解决了基本研究问题，从信息理论观点从评估关节OS和CS扭曲，开发OS和协作CS方案，以更好地绩效WSN，以及适应CS基于CS的WSN中的非均匀抽样的跨层设计，这将使该项目对理论和构建的理论和对机构的影响以及对机构的影响以及对机构的影响以及对机构的影响以及对机构的影响以及对机构的影响以及对机构的影响以及对机构的影响以及对机构的影响，并为WSN提供了实施。将招募来自不同社会的代表性不足和女学生，并通过IEEE向当地行业提供研讨会和内部短期课程。