The objective of this work is to generate new fundamental science that enables the operation of cyber-physical systems through complex environments. Predicting how a system will behave in the future requires more computing power if that system is complex. Navigating through environments with many obstacles could require significant computing time, which may delay the issue of decisions that have to be made by the on-board algorithms. Fortunately, systems do not always need the most accurate model to predict their behavior. This project develops new theory for deciding between the best model to use when making a decision in real time. The approach involves switching between different predictive models of the system, depending on the computational burden of the associated controller, and the accuracy that the predictive model provides. These tools will pave the way for more kinds of aircraft to navigate closely and safely with one another through the National Air Space (NAS), including Unmanned Air Systems (UAS). The results from this project will enable more accurate and faster trajectory synthesis for controllers with nonlinear plants, or nonlinear constraints that encode obstacles. The approach utilizes hybrid control to switch between models whose accuracy is normalized by their computational burden of predictive control methods. This synergistic approach enables computationally-aware cyber-physical systems (CPSs), in which model accuracy can be jointly considered with computational requirements. The project advances the knowledge on modeling, analysis, and design of CPSs that utilize predictive methods for trajectory synthesis under constraints in real-time cyber-physical systems. The results will include methods for the design of algorithms that adapt to the computational limitations of autonomous and semi-autonomous systems while satisfying stringent timing and safety requirements. With these methods come new tools to account for computational capabilities in real-time, and new hybrid feedback algorithms and prediction schemes that exploit computational capabilities to arrive at more accurate predictions within the time constraints. The algorithms will be modeled in terms of hybrid dynamical systems, to guarantee dynamical properties of interest. The problem space will draw from models of UAS in the NAS.

The goal of this project is to facilitate timely retrieval of dynamic situational awareness information from field-deployed nodes by an operational center in resource-constrained uncertain environments, such as those encountered in disaster recovery or search and rescue missions. This is an important cyber physical system problem with perspectives drawn at a system and platform level, as well as at the system of systems level. Technology advances allow the deployment of field nodes capable of returning rich content (e.g., video/images) that can significantly aid rescue and recovery. However, development of techniques for acquisition, processing and extraction of the content that is relevant to the operation under resource constraints poses significant interdisciplinary challenges, which this project will address. The focus of the project will be on the fundamental science behind these tasks, facilitated by validation via both in house experimentation, and field tests orchestrated based on input from domain experts. In order to realize the vision of this project, a set of algorithms and protocols will be developed to: (a) intelligently activate field sensors and acquire and process the data to extract semantically relevant information; (b) formulate expressive and effective queries that enable the near-real-time retrieval of relevant situational awareness information while adhering to resource constraints; and, (c) impose a network structure that facilitates cost-effective query propagation and response retrieval. The research brings together multiple sub-disciplines in computing sciences including computer vision, data mining, databases and networking, and understanding the scientific principles behind information management with compromised computation/communication resources. The project will have a significant broader impact in the delivery of effective situational awareness in applications like disaster response. The recent :World Disaster Report" states that there were more than 1 million deaths and $1.5 trillion in damage from disasters within the past decade; the research has the potential to drastically reduce these numbers. Other possible applications are law enforcement and environmental monitoring. The project will facilitate a strong inter-disciplinary education program and provide both undergraduate and graduate students experience with experimentation and prototype development. There will be a strong emphasis on engaging the broader community and partnering with programs that target under-represented students and minorities.

Many practical systems such as smart grid, unmanned aerial vehicles (UAVs) and robotic networks can be categorized as cyber physical systems (CPS). A typical CPS consists of physical dynamics, sensors, communication network and controllers. The communication network is of key importance in CPS, since it mimics the nerve system in the human body. Hence, it is critical to study how the communication network in CPS should be analyzed and designed. Essentially, communications stem from the uncertainty of system under consideration; random perturbations increase the system uncertainty, which is reduced by the control actions in CPS. It is well known that entropy is a measure of system uncertainty. A unified framework of entropy is used for CPS, in which random perturbations create entropy while communications and controls provide negative entropy to compensate the entropy generation. The intellectual merits are the novel framework of entropy for bridging the communications and control in CPS and the new design criterion based on the entropy of system state for CPS. The project's broader significance and importance are the education of various levels of students, the dissemination of results to public, and the impact on everyday life such as the improved agility and robustness of power grids. This project applies the framework of entropy to study the interdependencies of communications and control, thus facilitating the analysis and design of communications in CPS. The following tasks are tackled in the project: (a) Entropy Flow Based Communication Capacity Analysis in which communications in CPS is analyzed by studying the entropy fields in the physical dynamics, thus providing an estimation on the scale (bits/second) of communication capacity budget; (b) Communication Network Topology Design in which the design of the network topology (either physical or logical) is tackled through both optimization-based or heuristic approaches; (c) Online Network Resource Scheduling which refines the network resource scheduling during the operation using both optimization-based and heuristic approaches, within the framework of entropy fields; (d) Hardware Emulation Testbed which delivers a co-simulation testbed based on real time digital power simulator (RTDS) and a communication simulator, in the context of smart grids. Based on the research, new courses are developed. K-12 outreach and various levels of undergraduate/graduate educations are incorporated into the research.