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

CIF：SMALL：具有传感器网络的动态系统的分布式统计推断

• 批准号: 1017454
• 负责人: Alejandro Ribeiro
• 金额: $ 30万
• 依托单位: University of Pennsylvania
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2010
• 资助国家: 美国
• 起止时间: 2010-09-01 至 2014-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1017454&HistoricalAwards=false
• 关键词: CIF; SMALL; Distributed; Statistical; Inference

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.

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察结果来推断物理世界的属性。推理任务涵盖了很大的范围，特别是果园降雨量的估计，或跟踪海洋表面的盐度。本项目开发了一个集成框架，用于使用传感器网络进行动态过程的分布式统计推断。最终目标是影响众多活动，这些活动将从传感器网络的发展中受益，包括制造业，农业和环境管理等经济部门。将研究成果纳入本科课程预计会产生进一步影响，该项目推动使用价格作为媒介，将全球知识纳入当地估计。动态统计推断中的许多问题需要最优化问题的解决方案，促使公式，估计被视为经济产出最大化。集中式智能体的行为所导致的全局优化问题被视为社会资源优化问题。由个体传感器计算的局部估计是市场代理人自私行为的结果。价格的引入是为了使社会和代理商保持一致？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实现了这些想法。该研究横向跨越不同的动态统计推断问题，纵向分为：（i）价格调解算法的收敛特性的确定。(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）与学习算法的集成与不完整的模型信息的问题。