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

摘要传感器网络是连接虚拟世界和物理世界的分布式设备的交互集合。实现这些接口所需的任务之一是使用网络收集的观测数据推断物理世界的属性。推理任务涵盖了广泛的范围，具体的例子是估计果园的降雨量，或跟踪海洋的表面盐度。该项目开发了一个集成框架，用于使用传感器网络对动态过程进行分布式统计推断。最终目标是影响从传感器网络发展中受益的众多活动，包括制造业、农业和环境管理等经济部门。预计将研究成果纳入本科课程将产生进一步的影响。该项目推进了价格的使用，以调解将全球知识纳入当地估计。动态统计推理中的许多问题需要解决优化问题，从而促使将估计视为最大化经济产出的公式。中心化代理的全局优化被认为是一个社会资源优化问题。由单个传感器计算的局部估计是市场主体自私行为的结果。引入价格是为了使社会和代理人保持一致？的兴趣。虽然这些想法已经在静态环境中成功地实现了，但这个项目在动态环境中追求它们。本研究横向跨越不同的动态统计推理问题，纵向组织为：(1)确定价格中介算法的收敛性。（ii）通过操纵价格结构解决内存增长问题。（iii）实际考虑，包括鲁棒性、收敛速度和通信效果（iv）与模型信息不完整问题的学习算法集成。

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.