CPS: Synergy: Data Driven Intelligent Controlled Sensing for Cyber Physical Systems

交通、安全和制造应用中使用的网络物理系统依赖于各种传感器进行预测和控制。在这些系统中的许多系统中，信息的获取需要物理传感器的部署和激活，这可能导致增加的费用或延迟。这些系统的一个基本方面是，它们必须智能地寻求信息以支持它们的使命，并且必须确定关于物理测量的成本与信息的改进的最佳权衡。传感器和UAV技术的最近爆炸已经导致了通过改变激励水平、位置、方向、灵敏度、类似的参数。这反过来又给开发网络物理系统带来了巨大的挑战，这些系统可以有效地利用选择在哪里以及如何感知环境的自由度。这些挑战包括观测数据的高维性和相关的“维数灾难”、观测数据和潜在变量之间的非平凡关系、对与潜在感知行为的性质和所收集信息的相应价值相关的模型的理解不足，以及缺乏足够的训练数据来学习这些模型。智力优势：拟议的研究包括：（1）数据驱动的随机控制理论，用于网络物理系统中的智能感测，该理论结合了成本/延迟/风险，并考虑了感测，决策和预测模型不可用或知之甚少的情况。(2)在考古调查的真实的世界领域中无人机传感器网络控制方法的验证。更广泛的影响：拟议的努力包括：（a）推广：计划鼓励妇女和代表性不足的群体参与的努力;（B）社会影响：研究将导致在环境监测、交通监视和安全应用方面的新概念。(c)多学科活动：影响网络物理系统、传感器管理和统计学习中的现有知识。研究结果将通过会议介绍、部门研讨会、期刊论文、讲习班和在IEEE CDC和RSS的特别会议传播;（d）通过新的研究生课程和课程项目制定课程。

Cyber-physical systems employed in transportation, security and manufacturing applications rely on a wide variety of sensors for prediction and control. In many of these systems, acquisition of information requires the deployment and activation of physical sensors, which can result in increased expense or delay. A fundamental aspect of these systems is that they must seek information intelligently in order to support their mission, and must determine the optimal tradeoffs as to the cost of physical measurements versus the improvement in information.A recent explosion in sensor and UAV technology has led to new capabilities for controlling the nature and mobility of sensing actions by changing excitation levels, position, orientation, sensitivity, and similar parameters. This has in turn created substantial challenges to develop cyber-physical systems that can effectively exploit the degrees of freedom in selecting where and how to sense the environment. These challenges include high-dimensionality of observations and the associated "curse of dimensionality", non-trivial relationships between the observations and the latent variables, poor understanding of models relating the nature of potential sensing actions and the corresponding value of the collected information, and lack of sufficient training data from which to learn these models.Intellectual Merit: The proposed research includes: (1) data-driven stochastic control theory for intelligent sensing in cyber-physical systems that incorporates costs/delays/risks and accounts for scenarios where models for sensing, decision-making, and prediction are unavailable or poorly understood. (2) Validation of control methods on a UAV sensor network in the real world domain of archaeological surveying.Broader Impacts: The proposed effort includes: (a) Outreach: planned efforts for encouraging participation of women and under-represented groups; (b) Societal impact: research will lead to novel concepts in environmental monitoring, traffic surveillance, and security applications. (c) Multi- disciplinary activities: Impacting existing knowledge in cyber-physical systems, sensor management, and statistical learning. Research findings will be disseminated through conferences presentations, departmental seminars, journal papers, workshops and special sessions at IEEE CDC and RSS; (d) Curriculum development through new graduate level courses and course projects.