CAREER: Streaming Data Analysis in Sensor Networks

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This research aims to offer statistical foundation and a host of efficient scalable methodologies for streaming data analysis in sensor networks. In many applications, sensor networks are deployed to online monitoring of changing environments over time and space, with a goal of early detection of some particular trigger events that can cause significant damage. However, the nature of streaming data from distributed, diverse sources and the constrained network resources (on communication, computing, costs, privacy of raw data, etc.) pose significant challenges, which require the development of new statistical tools, methods and theories. In this project, the investigator proposes a novel general framework for monitoring sensor networks in which a trigger event may affect different sensors or data streams differently. Some specific research topics include pure (consensus or parallel) detection and inference after detection, under different scenarios, depending on the models for sensor observations and the design requirements of sensor protocols. In addition, the research will integrate research and education by infusing the research findings into the curriculum, by organizing seminars and workshops, and by advising graduate and undergraduate students.Senor networks have broad real-world applications, including but not limited to health and environmental monitoring, biomedical signal processing, wireless communication, intrusion detection in computer networks, and biosurveillance. On the one hand, this research project will offer crucial statistical tools to effectively and efficiently monitor and analyze dynamic data streams in these sensor network applications. On the other hand, it also has a frustrating yet profound implication in these applications: Faced with the limitations implied by the (asymptotic) optimality theories of the proposed research, practitioners and researchers may need to constantly look for better data sources to achieve desired system performance in their specific applications rather than relying on an improved methodology for existing data sources.