Cyber physical systems (CPSs) are merging into major mobile systems of our society, such as public transportation, supply chains, and taxi networks. Past researchers have accumulated significant knowledge for designing cyber physical systems, such as for military surveillance, infrastructure protection, scientific exploration, and smart environments, but primarily in relatively stationary settings, i.e., where spatial and mobility diversity is limited. Differently, mobile CPSs interact with phenomena of interest at different locations and environments, and where the context information (e.g., network availability and connectivity) about these physical locations might not be available. This unique feature calls for new solutions to seamlessly integrate mobile computing with the physical world, including dynamic access to multiple wireless technologies. The required solutions are addressed by (i) creating a network control architecture based on novel predictive hierarchical control and that accounts for characteristics of wireless communication, (ii) developing formal network control models based on in-situ network system identification and cross-layer optimization, and (iii) designing and implementing a reference implementation on a small scale wireless and vehicular test-bed based on law enforcement vehicles. The results can improve all mobile transportation systems such as future taxi control and dispatch systems. In this application advantages are: (i) reducing time for drivers to find customers; (ii) reducing time for passengers to wait; (iii) avoiding and preventing traffic congestion; (iv) reducing gas consumption and operating cost; (v) improving driver and vehicle safety, and (vi) enforcing municipal regulation. Class modules developed on mobile computing and CPS will be used at the four participating Universities and then be made available via the Web.

This project designs algorithms for the integration of plug-in hybrid electric vehicles (PEVs) into the power grid. Specifically, the project will formulate and solve optimization problems critical to various entities in the PEV ecosystem -- PEV owners, commercial charging station owners, aggregators, and distribution companies -- at the distribution / retail level. Charging at both commercial charging stations and at residences will be considered, for both the case when PEVs only function as loads, and the case when they can also function as sources, equipped with vehicle-to-home (V2H) or vehicle-to-grid (V2G) energy reinjection capability. The focus of the project is on distributed decision making by various individual players to achieve analytical system-level performance guarantees. Electrification of the transportation market offers revenue growth for utility companies and automobile manufacturers, lower operational costs for consumers, and benefits to the environment. By addressing problems that will arise as PEVs impose extra load on the grid, and by solving challenges that currently impede the use of PEVs as distributed storage resources, this research will directly impact the society. The design principles gained will also be applicable to other cyber-physical infrastructural systems. A close collaboration with industrial partners will ground the research in real problems and ensure quick dissemination of results to the marketplace. A strong educational component will integrate the proposed research into the classroom to allow better training of both undergraduate and graduate students. The details of the project will be provided at http://ee.nd.edu/faculty/vgupta/research/funding/cps_pev.html

This project demonstrates the synergistic use of a cyber-physical infrastructure consisting of smart-phone devices; cloud computing, wireless communication, and intelligent transportation systems to manage vehicles in the complex urban network -- through the use of traffic controls, route advisories and road pricing -- to jointly optimize drivers' mobility and the sustainability goals of reducing energy usage and improving air quality. The system developed, MIDAS-CPS, proactively manages the interacting traffic demand and the available transportation supply. A key element of MIDAS-CPS is the data collection and display device PICT that collects each participating driver's vehicle position, forward images from the vehicle's dashboard, and communication time stamps, and then displays visualizations of predicted queues ahead, relevant road prices, and route advisories. Given the increasing congestion in most of the urban areas, and the rising costs of constructing traffic control facilities and implementing highway hardware, MIDAS-CPS could revolutionize the way traffic is managed on the urban network since all computing is done via clouds and the drivers instantly get in-vehicle advisories with graphical visualizations of predicted conditions. It is anticipated this would lead to improved road safety and lesser drive stress, besides the designed benefits on the environment, energy consumption, congestion mitigation, and driver mobility. This multidisciplinary project is at the cutting edge in several areas: real-time image processing, real-time traffic prediction and supply/demand management, and cloud computing. Its educational impacts include enhancements of curricula and laboratory experiences at participating universities, workforce development, and student diversity.

This project demonstrates the synergistic use of a cyber-physical infrastructure consisting of smart-phone devices; cloud computing, wireless communication, and intelligent transportation systems to manage vehicles in the complex urban network ? through the use of traffic controls, route advisories and road pricing ? to jointly optimize drivers? mobility and the sustainability goals of reducing energy usage and improving air quality. The system developed, MIDAS-CPS, proactively manages the interacting traffic demand and the available transportation supply. A key element of MIDAS-CPS is the data collection and display device PICT that collects each participating driver?s vehicle position, forward images from the vehicle?s dashboard, and communication time stamps, and then displays visualizations of predicted queues ahead, relevant road prices, and route advisories. Given the increasing congestion in most of the urban areas, and the rising costs of constructing traffic control facilities and implementing highway hardware, MIDAS-CPS could revolutionize the way traffic is managed on the urban network since all computing is done via clouds and the drivers instantly get in-vehicle advisories with graphical visualizations of predicted conditions. It is anticipated this would lead to improved road safety and lesser drive stress, besides the designed benefits on the environment, energy consumption, congestion mitigation, and driver mobility. This multidisciplinary project is at the cutting edge in several areas: real-time image processing, real-time traffic prediction and supply/demand management, and cloud computing. Its educational impacts include enhancements of curricula and laboratory experiences at participating universities, workforce development, and student diversity. Additional information on the project is available at http://midas-cps.mobicloud.asu.edu/.

This project aims to achieve key technology, infrastructure, and regulatory science advances for next generation medical systems based on the concept of medical application platforms (MAPs). A MAP is a safety/security-critical real-time computing platform for: (a) integrating heterogeneous devices and medical IT systems, (b) hosting application programs ("apps") that provide medical utility through the ability to both acquire information and update/control integrated devices, IT systems, and displays. The project will develop formal architectural and behavioral specification languages for defining MAPs, with a focus on techniques that enable compositional reasoning about MAP component interoperability and safety. These formal languages will include an extensible property language to enable the specification of real-time, quality-of-service, and attributes specific to medical contexts that can be leveraged by code generation, testing, and verification tools. The project will work closely with a synergistic team of clinicians, device industry partners, regulators, and medical device interoperability and safety standard organizations to develop an open source MAP innovation platform to enable key stakeholders within the nation's health care ecosphere to identify, prototype, and evaluate solutions to key technology and regulatory challenges that must be overcome to develop a commodity market of regulated MAP components. Because MAPs provide pre-built certified infrastructure and building blocks for rapidly developing multi-device medical applications, this research has the potential to usher in a new paradigm of medical system that significantly increases the pace of innovation, lowers development costs, enables new functionality by aggregating multiple devices into a system of systems, and achieves greater system safety.

Large-scale critical infrastructure systems, including energy and transportation networks, comprise millions of individual elements (human, software and hardware) whose actions may be inconsequential in isolation but profoundly important in aggregate. The focus of this project is on the coordination of these elements via ubiquitous sensing, communications, computation, and control, with an emphasis on the electric grid. The project integrates ideas from economics and behavioral science into frameworks grounded in control theory and power systems. Our central construct is that of a ?resource cluster,? a collection of distributed resources (ex: solar PV, storage, deferrable loads) that can be coordinated to efficiently and reliably offer services (ex: power delivery) in the face of uncertainty (ex: PV output, consumer behavior). Three topic areas form the core of the project: (a) the theoretical foundations for the ?cluster manager? concept and complementary tools to characterize the capabilities of a resource cluster; (b) centralized resource coordination strategies that span multiple time scales via innovations in stochastic optimal control theory; and (c) decentralized coordination strategies based on cluster manager incentives and built upon foundations of non-cooperative dynamic game theory. These innovations will improve the operation of infrastructure systems via a cyber-physical-social approach to the problem of resource allocation in complex infrastructures. By transforming the role of humans from passive resource recipients to active participants in the electric power system, the project will facilitate energy security for the nation, and climate change mitigation. The project will also engage K-12 students through lab-visits and lectures; address the undergraduate demand for power systems training through curricular innovations at the intersection of cyber-systems engineering and physical power systems; and equip graduate students with the multi-disciplinary training in power systems, communications, control, optimization and economics to become leaders in innovation.

1239257 (Wu). Traditionally, buildings have been viewed as mere energy consumers; however, with the new power grid infrastructure and distributed energy resources, buildings can not only consume energy, but they can also output energy. As a result, this project removes traditional boundaries between buildings in the same cluster or between the cluster and power grids, transforming individual smart buildings into NetZero building clusters enabled by cyber-support tools. In this research, a synergistic decision framework is established for temporally, spatially distributed building clusters to work as an adaptive and robust system within a smart grid. The framework includes innovative algorithms and tools for building energy modeling, intelligent data fusion, decentralized decisions and adaptive decisions to address theoretical and practical challenges in next-generation building systems. The research develops cyber-physical engineering tools for demand side load management which has been identified as a major challenge by energy industries. It fundamentally transforms the current centralized and uni-directional power distribution business model to a decentralized and multi-directional power sharing and distribution business model, reducing overall energy consumption and allowing for optimal decisions in changing operation environments. Education and outreach efforts include developing novel educational modules disseminated at the K-12 levels and through the ASEE eGFI repository. Further educational impact occurs through integration with multiple undergraduate and graduate courses at each institution, and with community service groups. Impact is also expanded to the broader energy industry and the operation of healthcare delivery and urban transportation systems through our industry collaborations. http://swag.engineering.asu.edu/ 1239247 (Wen). Traditionally, buildings have been viewed as mere energy consumers; however, with the new power grid infrastructure and distributed energy resources, buildings can not only consume energy, but they can also output energy. As a result, this project removes traditional boundaries between buildings in the same cluster or between the cluster and power grids, transforming individual smart buildings into NetZero building clusters enabled by cyber-support tools. In this research, a synergistic decision framework is established for temporally, spatially distributed building clusters to work as an adaptive and robust system within a smart grid. The framework includes innovative algorithms and tools for building energy modeling, intelligent data fusion, decentralized decisions and adaptive decisions to address theoretical and practical challenges in next-generation building systems. The research develops cyber-physical engineering tools for demand side load management which has been identified as a major challenge by energy industries. It fundamentally transforms the current centralized and uni-directional power distribution business model to a decentralized and multi-directional power sharing and distribution business model, reducing overall energy consumption and allowing for optimal decisions in changing operation environments. Education and outreach efforts include developing novel educational modules disseminated at the K-12 levels and through the ASEE eGFI repository. Further educational impact occurs through integration with multiple undergraduate and graduate courses at each institution, and with community service groups. Impact is also expanded to the broader energy industry and the operation of healthcare delivery and urban transportation systems through our industry collaborations. http://swag.engineering.asu.edu/ 1239093 (Lewis). Traditionally, buildings have been viewed as mere energy consumers; however, with the new power grid infrastructure and distributed energy resources, buildings can not only consume energy, but they can also output energy. As a result, this project removes traditional boundaries between buildings in the same cluster or between the cluster and power grids, transforming individual smart buildings into NetZero building clusters enabled by cyber-support tools. In this research, a synergistic decision framework is established for temporally, spatially distributed building clusters to work as an adaptive and robust system within a smart grid. The framework includes innovative algorithms and tools for building energy modeling, intelligent data fusion, decentralized decisions and adaptive decisions to address theoretical and practical challenges in next-generation building systems. The research develops cyber-physical engineering tools for demand side load management which has been identified as a major challenge by energy industries. It fundamentally transforms the current centralized and uni-directional power distribution business model to a decentralized and multi-directional power sharing and distribution business model, reducing overall energy consumption and allowing for optimal decisions in changing operation environments. Education and outreach efforts include developing novel educational modules disseminated at the K-12 levels and through the ASEE eGFI repository. Further educational impact occurs through integration with multiple undergraduate and graduate courses at each institution, and with community service groups. Impact is also expanded to the broader energy industry and the operation of healthcare delivery and urban transportation systems through our industry collaborations.

Effective engineering of complex devices often depends critically on the ability to encapsulate responsibility for tasks into modular agents and ensure those agents communicate with one another in well-defined and easily observable ways. When such conditions are followed, it becomes possible to detect where problems lie so they can be corrected. It also becomes possible to optimize the agents and their communications to improve performance. Cyber-physical systems (like robots, self-piloting aircraft, etc.) modify themselves to improve performance break those conditions in that some agent modules negotiate their own communications and decide their own actions, sometimes taking advantage of the physics of the world in ways we did not anticipate. This renders difficult application of standard engineering tools to accomplish critical fault diagnosis and design optimization. This project will produce analysis methods address the specific needs of cyber-physical systems that, by their natures, break the rules of convention. We will apply these new methods to the design and analysis of self-improving controllers for flapping-wing micro air vehicles. This work will provide advances in both model-checking related formal design methodologies and in module-based self-adaptive control in computationally resource constrained cyber-physical systems. The formal methods advances will significantly expand our ability to properly design and verify systems that tightly couple computation, sensors, and actuators. The specific test application addressed is significant to a number of nationally important security and defense efforts and will directly impact identified national priorities.

Cyber physical systems (CPSs) are merging into major mobile systems of our society, such as public transportation, supply chains, and taxi networks. Past researchers have accumulated significant knowledge for designing cyber physical systems, such as for military surveillance, infrastructure protection, scientific exploration, and smart environments, but primarily in relatively stationary settings, i.e., where spatial and mobility diversity is limited. Differently, mobile CPSs interact with phenomena of interest at different locations and environments, and where the context information (e.g., network availability and connectivity) about these physical locations might not be available. This unique feature calls for new solutions to seamlessly integrate mobile computing with the physical world, including dynamic access to multiple wireless technologies. The required solutions are addressed by (i) creating a network control architecture based on novel predictive hierarchical control and that accounts for characteristics of wireless communication, (ii) developing formal network control models based on in-situ network system identification and cross-layer optimization, and (iii) designing and implementing a reference implementation on a small scale wireless and vehicular test-bed based on law enforcement vehicles. The results can improve all mobile transportation systems such as future taxi control and dispatch systems. In this application advantages are: (i) reducing time for drivers to find customers; (ii) reducing time for passengers to wait; (iii) avoiding and preventing traffic congestion; (iv) reducing gas consumption and operating cost; (v) improving driver and vehicle safety, and (vi) enforcing municipal regulation. Class modules developed on mobile computing and CPS will be used at the four participating Universities and then be made available via the Web.