Distributed Methods for Statistical Decision Making in Networked Environments

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Abstract: Recent advances in sensor and computing technologies enable massively distributed networks of sensors as candidate technologies to provide real-time information in diverse applications such as building safety, environmental sensing, habitat monitoring, power systems and manufacturing. The potential of networked sensing by such devices is enormous, yet there are significant system-level challenges in realizing this potential by designs that are reliable, efficient and scalable.This research involves the development of fundamental systems-level framework for uncertain decision making in ad-hoc networked and power limited environments. Although some of these issues have beenpreviously tackled in isolation, we will develop a unified approach, addressing multiple interacting dependencies to obtain systems level tools for rational design and implementation. Accordingly this research has three principle thrusts: 1) Adaptive Querying Strategies (AQS) -- a top-down viewpoint based on hierarchical architectures that involve designated decision agents(s). In this thrust we propose novel feedback strategies for sensor querying at low data rates (low granularity). 2) Distributed Detection -- a bottom-up approach which amounts to consensus building via local message passing on sub-networks. 3) Distributed Estimation -- Strategies for scaling information granularity from a set of known sensors to known destinations under operational constraints is proposed. The novelty of this approach is to develop tools for sensor fusion for transmitting information about non-ergodic, non-random parameters observed in stochastic noise.