CIF: Small: Collaborative Research: Distributed Detection Algorithms and Stochastic Modeling for Large Monitoring Sensor Networks

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Operational and safety goals for the built environment demand robust, scalable and reliable largescale monitoring for infrastructure systems. High performance real-time event detection and decision makingrequires models and algorithms to process large amounts of data from dense sensor networks deployedin these systems. Despite advances in the development of detection algorithms for such networks, there aretwo widely recognized and conflicting obstacles: detection rules need to be sufficiently complex to adapt tothe spatiotemporal changes in the environment, requiring the sharing of data; but rules are constrained bystatistical performance guarantees and computation and communicational budgets imposed by the network.This project addresses these challenges by developing a fundamentally new approach that jointly accountsfor statistical detection, communication constraints and distributed computation.This research develops a framework that integrates the distributed computation and communication constraintsof the underlying network infrastructure with flexible stochastic modeling and learning algorithmswith spatiotemporal data. The modeling and algorithms enable simultaneous and sequential decision makingat many local sites, by borrowing information across the network in a statistically coherent and computationallyefficient manner. Combining the formalism of sequential change point detection, nonparametric andprobabilistic graphical models and spatiotemporal statistics, the project develops distributed and sequentialmessage-passing algorithms for detecting changes in the underlying distributions generating network data.The models developed also offer new theoretical understanding of the trade-offs between statistical modelcomplexity, distributed computation efficiency, and structure of communication constraints within the network.This interdisciplinary research brings together students and researchers from different areas, utilizingand developing knowledge and cross-disciplinary skills in the fields of computer science, statistics, signalprocessing and civil engineering.