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.
