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

该协作项目研究了无线传感器网络中的机会感知和压缩感知。操作系统指的是一种范例，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，从而产生一个自适应网络，该网络可以自动发现依赖于场景、目标驱动的机会，并具有优化的性能。CS是一种新的传感/采样范式，它与数据采集中的普遍智慧背道而驰。操作系统和CS都有助于显著提高无线传感器网络的运行效率和性能。特别是，OS的目标是通过选择传感器和模式的子集来进行有效的数据融合，而CS的目标是通过非均匀地选择样本的子集来减少采样。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是至关重要的，它不仅可以确保感知资产的有效利用，还可以提供稳健的最优性能。本课题从信息论的角度对无线传感器网络中的联合OS和CS失真进行评估，为提高无线传感器网络的性能开发OS和协同CS方案，并针对基于CS的无线传感器网络中的非均匀采样进行跨层设计，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要贡献，并将在国土安全和国防方面产生广泛而深远的社会影响。将招募来自不同社会的代表不足的女学生，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.