CIF: SMALL: Distributed Statistical Inference of Dynamic Systems with Sensor Networks

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AbstractSensor networks are interactive collections of distributed devices that interface the virtual and physical worlds. Among the tasks needed to implement these interfaces is the inference of properties of the physical world using observations collected by the network. Inference tasks cover a vast range, particular examples being estimation of rainfall in an orchard, or tracking the surface salinity of the ocean. This project develops an integrated framework for distributed statistical inference of dynamic processes using sensor networks. The ultimate goal is to impact on the numerous activities that stand to benefit from the development of sensor networks, including economic sectors like manufacture, agriculture, and environmental management. Further impact is expected from the incorporation of research results in undergraduate classes.This project advances the use of prices to mediate the incorporation of global knowledge into local estimates. Many problems in dynamic statistical inference require solution of optimization problems, prompting formulations whereby estimates are viewed as economic outputs to be maximized. The global optimization that would result from the action of a centralized agent is regarded as a social resource optimization problem. Local estimates computed by individual sensors are the result of selfish actions of market agents. Prices are introduced to align social and agents? interest. While these ideas have been successfully pursued in static environments, this project pursues them in dynamic settings. The research cuts horizontally across different dynamic statistical inference problems and is vertically organized into: (i) Determination of convergence properties of price mediation algorithms. (ii) Resolution of memory growth problems through manipulation of price structures. (iii) Practical considerations including robustness, convergence speed, and communication effects (iv) Integration with learning algorithms for problems with incomplete model information.