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

该合作项目研究了无线传感器网络（wsn）中的机会感知（OS）和压缩感知（CS）。OS指的是一种范例，在这种范例中，WSN可以根据操作场景自动发现和选择传感器模式和传感器，从而形成一个自适应网络，自动发现依赖于场景的、目标驱动的机会，并具有优化的性能。CS是一种新颖的传感/采样范式，与数据采集中的普遍智慧背道而驰。OS和CS都有助于显著提高wsn的运行效率和性能。特别是，OS的目标是通过选择有效数据融合的传感器和模式子集来实现空间约简，而CS的目标是通过非均匀地选择样本子集来实现采样的约简。因此，机会感知和压缩感知的理论基础和算法对于提高无线传感器网络的技术水平至关重要，这不仅确保了感知资产的有效利用，而且提供了鲁棒的最佳性能。本课题从信息论的角度研究OS和CS联合畸变的评估、开发OS和协同CS方案以提高无线传感器网络的性能、跨层设计以适应基于CS的无线传感器网络的非均匀采样。该项目将为无线传感器网络的机会感知和压缩感知的理论和应用做出重大贡献，并将在国土安全和国防领域产生广泛而深刻的社会影响。将招募来自不同社团的弱势学生和女学生，并通过IEEE向当地行业提供研讨会和内部短期课程。

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.