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.
