Peter Beling - US grants

As is clear from the emergence of the Internet, the national electric power grid, and other large-scale network infrastructures, engineering systems today are increasingly reliant on distributed control authority and coordination between subsystems. Typically, coordination is achieved in engineering systems through the specification of ad hoc protocols for relatively well-defined (constrained) interactions between distributed systems. As information systems become more integrated into society, however, we find that existing protocols are not adequately tuned for new applications and/or unexpected situations. Though issues of decentralized control and planning are becoming more prevalent in the engineering systems we build today, there unfortunately appears to be little in the way of underlying guiding principles and theory for designing and operating such systems. Notions of game theory and decentralized control go only part way toward revealing the basic problems associated with distributed engineering systems, especially in situations where distributed agents/players/actors all recognize the same performance objective and would work together except for the problem of having little or no opportunities to coordinate their actions because of limited communication. In this project, the PI's goal is to derive an enhanced understanding of coordination without explicit communication by posing a new class of sequential decision processes, known as coordination processes, whose analysis will provide new theoretical insights and new algorithmic approaches in decentralized systems and distributed planning applications. The mathematical framework the PI team will investigate is rooted in the theory of controlled Markov processes and dynamic games, thus providing a firm foundation for new results, including new solution concepts and new algorithmic approaches for identifying optimal coordination strategies. The PI team will focus their efforts broadly on two subclasses of coordination problems: transient coordination processes where all actors seek to drive an underlying system to a terminal state with minimum cost; repeated play coordination processes where the objective is to learn optimal coordination strategies that tend to minimize the average cost perceived in repeated instances of a coordination problem. The PI team will evaluate their solution concepts and algorithmic procedures in the context of two illustrative applications, a robotic planning problem and an Internet traffic engineering problem, both of which require autonomous agents (actors) to coordinate without opportunities for explicit communication.

Broader Impact: This work will impact diverse research communities, including those for control theory, game theory, and decision sciences. The results should find application in diverse sectors
of engineering and computer science, including the design of new MAC-layer protocols, the design of better conflict resolution algorithms in distributed collaboration tools and peer-to-peer applications, the design and control of transportation resource management systems, and in future sensor management systems.

The University of Virginia WICAT research site focuses on technology to support Rapid Configuration of Wireless Systems. The proposed research plan will focus on two activities important to serving mobile users in a large enterprise that requires user security. The proposed research relates to the special interests of the National Reconnaissance Office who is a member of the WICAT I/UCRC at the University of Virginia.

A successful completion of the proposed research will make a significant contribution to improving the knowledge base for secure mobile computing. The proposed activities will provide all sites with an expanded set of data and results to complement their component technology results. The proposed effort will also broaden currently provided opportunities to achieve greater diversity by creating additional invited student interactions across the WICAT research institutions.

This research will address the feasibility of enabling teachers at the high school and college levels to evaluate how well their students are learning via an automated analysis of data collected through technology-based instrumentation of their classrooms. The data will include audio/video collections on interactive class room behaviors, which are related to established performance markers. Results of data collections will be integrated into a DVD presentation that will produce feedback for the teacher for self-evaluation with the opportunity for self-improvement, or incorporation into guided coaching systems. The effort will build upon prior efforts from three different research communities: education, military intelligence, and Internet technology. This research will advance video processing that focuses on automated behavior pattern analysis related to a class room. It will also increase understanding of the relationship between previously developed manual class room observation metrics for helping teachers to improve and corresponding metrics gathered from an array of class room sensors. The conduct of this work will lead to identification of yet to be considered advances in the application of audio/video technology to classroom monitoring and teacher self improvement as well as the opening of innovative lines of research on classroom settings.

The proposed effort initiates a new and important research collaboration at the University of Virginia integrating the work of researchers who have been engaged in improving education at the Center for Advanced Study of Teaching and Learning and researchers who have been engaged in future applications of advanced wireless sensor technology at the Wireless Internet Center for Advanced Technology, an NSF sponsored Industry/University Cooperative Research Center. This collaboration is considered to be an important element in advancing the concept of technology-based class room evaluation. If successful, this effort will undoubtedly spawn a variety of opportunities for improvement of the initial concept and the corresponding development of new production systems.

Wireless Internet Center for Advanced Technology (WICAT)
Proposal #1127960

This proposal seeks funding for the Wireless Internet Center for Advanced Technology (WICAT) sites at the Polytechnic Institute of NYU (lead), Auburn University, the University of Virginia, and the Virginia Polytechnic Institute and State University. Funding Requests for Fundamental Research are authorized by an NSF approved solicitation, NSF 10-601. The solicitation invites I/UCRCs to submit proposals for support of industry-defined fundamental research.

Increasing demand for video services on handheld and other wireless devices has made efficient exchange of video data using wireless devices a necessity. However the continual changes in user demand, network traffic and hardware of the wireless environment pose significant research challenges to achieving this goal. The proposed research seeks to address these challenges via a three pronged effort to explore wireless technologies able to adapt to changing communication system states. A test bed environment for the conduct of the proposed work that will provide system surveillance and cloud computing infrastructure will be provided by Cisco Systems.

Efficient and seamless use of video on wireless platforms in the emerging cloud computing environment will have major economic impact in virtually all economic sectors. Ubiquitous implementation and widespread adoption of these services will also create societal impact through the degree of communication it will enable. The proposed work has the potential to inform and define approaches to achieving this video content integration. The work has the potential to strengthen collaborative ties and link resources among the four participating center sites to further both research and education in this critical area.

The proposed center seeks to establish a new Industry/University Cooperative Research Center (I/UCRC) addressing broadband wireless access and applications. The center will focus on four areas: 1) enabling sharing up to many GHz of spectrum using technological advances and enabling platforms such as spectrum trading and auctions, and millimeter wave communications; 2) co-existence of heterogeneous devices (e.g. radar and communication systems); 3) providing connectivity for new applications such as wireless devices in the hospitals of the future, bringing wireless into the hospital in novel ways; and (d) improving electronic warfare technologies that address national-security issues.

The new center addresses an area of critical economic and has the potential to support development of broadband wireless as a platform for innovation as addressed in the White House PCAST Report. The proposed center has the potential to link a diverse set of member companies across the broadband industry sector with university discovery in this area. Moreover, the center participants have a history of start-up initiation, and the opportunity from new starts to leverage the ecosystem created by the center is significant. The center plans to impact students via their conduct of center research, curriculum development and REU site development.

The University of Virginia (UVA) is planning to join the Center for Dynamic Data Analytics, an existing multi-university Industry/University Cooperative Research Center (I/UCRC) which is currently comprised of Rutgers University (lead institution) and Stony Brook University. This award provides UVA with the support from the NSF for exploring the feasibility of establishing this new site. The planning grant will be used to plan a joint industry and university research agenda, to organize and attend the meetings with potential industry members and the existing sites, and to host a planning meeting that will be the center point of the effort to articulate the vision for the site to potential members. The principal industry domains for UVA/CDDA research are: (1) Finance and banking, including financial markets and electronic trading, systemic risk, and data analytics for regulators; (2) Internet analytics, including marketing and the Internet of Things; (3) Advanced manufacturing, including data-driven approaches to automation, human factors, process improvement, and prognostics; (4) Cyberphysical systems, including cybersecurity of physical systems and autonomous systems. Together these domains constitute a portion of the economy that is both substantial and increasing.

The primary mission of the UVA site of CDDA is to perform research on data analytics to support decision making in industry and government. The focus is on analysis of data sets distinguished by their velocity, variety, volume, complexity, and other features commonly associated with Big Data. The UVA site of CDDA will pursue a research agenda that complements that of the existing sites and features an integration of control, decision, and systems modeling concepts with statistical methods, machine learning and pattern recognition. Areas of concentration include behavioral and preference modeling, state-based models, reinforcement learning, secure computation and data privacy, and data-driven approaches to cybersecurity. A principal distinguishing characteristic of the UVA site research agenda is an emphasis on prescriptive modeling and understanding the collection and use of data in the context of decision making in complex systems.

The Center for Visual and Decision Informatics (CVDI) National Science Foundation Industry University Cooperative Research Center (IUCRC) was established in 2012 by the University of Louisiana at Lafayette (UL Lafayette) and Drexel University. CVDI has been successful in meeting the IUCRC program management requirements during its Phase 1, with demonstrated excellent growth trajectory in terms of research funding, industry members, and academic sites, and will continue into Phase 2 operation. This award aims to help establish a Phase II CVDI site at the University of Virginia (UVA) that complements and strengthens the research base and industrial support of the existing sites of CVDI. The Center's activities will generate societal benefits by addressing areas of national priority such as finance, cyber-physical systems, and security, and will deliver data science innovations with value to both government and industry. UVA CVDI will follow a strategy to broaden Science, Technology, Engineering, and Mathematics (STEM) participation: (1) involve under-represented groups through the NSF Research Experience for Undergraduates program; (2) recruit graduate students from neighboring Historically Black Colleges and Universities; (3) collaborate with K-12 STEM programs for data science outreach; and (4) increase student internships opportunities by leveraging CVDI industrial members and national initiatives, as the Graduate Education for Minorities program.

CVDI will serve as a major research and innovation center for solving challenges in core areas of data science such as big data analytics, visual analytics, augmented intelligence, and decision informatics. The UVA site brings complementary and synergistic research strength in a number of important technical areas, including: cognitive assistance, information retrieval, natural language processing, latent structure learning, reinforcement learning, data fusion and distributed learning, and agent-based models and simulation. The UVA site's focus on decision analytics and human-in-the-loop data gathering, analysis, and exploitation complements the activities and interests of the other sites. Application domains for the UVA site also reinforce and complement those of the other sites. These include: computational law, manufacturing, IoT and sensing, data privacy and security, finance and banking, internet analytics, and national defense.