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

本合作计画主要研究无线感测网路中的解析感测与压缩感测。OS指的是一种范例，在这种范例中，WSN可以基于操作场景自动发现和选择传感器模态和传感器，从而形成一种自适应网络，该网络可以自动找到依赖于网络的目标驱动的机会，并具有优化的性能。CS是一种新颖的传感/采样范例，它违背了数据采集中的普遍智慧。 操作系统和CS有助于提高无线传感器网络的有效操作和性能显着。特别是，OS的目的是减少从空间通过选择一个子集的传感器和模态进行有效的数据融合，而CS的目标减少采样通过选择一个子集的样本不均匀。 因此，机会感知和压缩感知的理论基础和算法对于推进无线传感器网络的最新技术水平至关重要，不仅可以确保有效利用传感资产，还可以提供鲁棒的最佳性能。 本计画从资讯论的观点探讨基础研究议题，以评估操作系统与压缩感知的失真，发展操作系统与压缩感知的合作机制，以提升无线传感器网路的效能，和跨层设计，以适应CS中的非均匀采样，该项目将对机会感知和压缩感知在无线传感器网络中的理论和应用做出重大贡献，并将对无线传感器网络产生广泛而深刻的社会影响。国土安全部和国防部将招募来自不同社会的代表性不足的女学生，并通过IEEE为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.