Goce Trajcevski, Ph.D. - US grants

Wireless sensor networks (WSNs) composed of smart sensors interconnected over wireless links are quickly becoming the technology of choice for monitoring and measuring geographically distributed physical, chemical, or biological phenomena in real time. Dynamic WSN environments encountered in environmental monitoring, surveillance, pollution control, and reconnaissance applications, require responsive management of WSN resources and their adaptive allocation to sensing, networking support, localization, and planning tasks, based on user requests and changes in the environment. A specified quality of service should however be ensured for criteria such as resolution of the raw-data, latency, network reconfiguration delay, and resource utilization in the steady-state. This project develops an integrated cross-layered approach to networking, databases, control, mobility management, and information processing in WSNs. In particular, context-aware and energy-efficient solutions are pursued that are based on opportunistic sensing and processing techniques, dynamic indexing structures, novel query language constructs, reactive mobility control algorithms, and distributed compression based routing algorithms.

The technological advances from this research will significantly simplify the deployment of WSNs and lead to novel context-aware applications. The advances will directly benefit domains such as emergency-response management, environmental threat remediation, and biological habitat monitoring. Apart from developing the required algorithms, the project will implement simulation platforms and a monitoring environment using physical devices. The platforms will provide students with new educational opportunities to actively explore information acquisition and resource management in resource-constrained environments. All project resources will be shared with the public through a project webpage.

Researchers at Florida International University (IIS-1213026), University of Illinois at Chicago (IIS-1213013), Brown University (IIS-1212508), and Northwestern University (IIS-1213038) are developing a high-performance model for information processing and fusion in mobile environments, providing a collaborative integration between the real and virtual worlds. This model, applicable to the fields of computational transportation and mobile sensing, enables querying and visualization of moving objects data (MOD) and their relationship to static and dynamic geospatial data. Research project addresses the issues of: balancing the processing of location-based data streams coming into MOD servers with efficient processing of visualization-related queries; determining optimal distribution of queries/tasks among multiple regional servers; maximizing the scalability of prediction techniques in terms of efficient management of objects' data and queries; modeling data uncertainty; coupling map generalization with trajectories' data reduction when zooming across different scales; resolving issues of privacy and security; and enabling semantic querying. A demonstration of the outcomes is available within the TerraFly testbed (http://TerraFly.fiu.edu) -- a public Geographic Information System (GIS) mapping engine and location-based data repository.

This work explores the novel steps towards combining the real and virtual worlds, an emerging research frontier. The virtual world is relatively well understood, but the combination of the real and virtual poses great challenges and promises transformative results with high potential payoff, including in-car navigation systems, massive fleets of mobile sensors, self-navigating vehicles, situation command, and location-based services. While advancing Computer Science, the project also leverages prior investment of, and provides direct benefit to, NSF, NASA, DoI, DoT, DHS, and other stakeholders such as the NSF EarthCube project. By improving the efficiency of spatial, temporal, and moving object data management and making these results available to constituencies via TerraFly, EarthCube and other venues, the project will produce societal benefits. This project provides a foundation for improving the quality of services in multiple applications such as disaster management, environmental monitoring, transportation, education, and logistics. The resulting technologies may serve as a base to advance research on self-navigating vehicles, robots, and mobile sensors. In particular, this work facilitates the technologies of Informed Traveler Programs, dynamic navigation, situation control, and airborne observational systems. The project provides rich educational and research opportunities for students from the collaborating institutions -- including underrepresented students. In addition, educational modules are developed, and research results will be incorporated in curriculum expansions. Further information is available at the project's website (http://CAKE.fiu.edu/MOD).

One of the challenges toward achieving the vision of smart cities is improving the state of the underground infrastructure. For example, large US cities have thousands of miles of aging water mains, resulting in hundreds of breaks every year, and a large percentage of water consumption that is unaccounted for. The goal of this project is to develop models and methods to generate, analyze, and share data on underground infrastructure systems, such as water, gas, electricity , and sewer networks. The interdisciplinary team of investigators from the University of Illinois at Chicago, Brown University, and Northwestern University will leverage partnerships with the cities of Chicago and Evanston, Illinois, to make the approach and findings relevant to their stakeholders. Research results will be incorporated in courses at the three institutions. Outreach efforts include events for K-12 students to develop awareness about underground infrastructure from a data and computational perspective. The results of the project will ultimately help municipalities maintain and renovate civil infrastructure in a more effective manner.

Cities are cyber-physical systems on a grand scale, and developing a precise knowledge of their infrastructure is critical to building a foundation for the future smart city. This proposal takes an information centric approach based on the complex interaction among thematic data layers to developing, visualizing, querying, analyzing, and providing access to a comprehensive representation of the urban underground infrastructure starting from incomplete and imprecise data. Specifically, the project has the following main technical components: (1) Generation of accurate GIS-based representations of underground infrastructure systems from paper maps, CAD drawings, and other legacy data sources; (2) Visualization of multi-layer networks combining schematic overview diagrams with detailed geometric representations; (3) Query processing algorithms for integrating spatial, temporal, and network data about underground infrastructure systems; (4) Data analytics spanning heterogeneous geospatial data sources and incorporating uncertainty and constraints; (5) Selective access to stakeholders on a need-to-know basis and facilitating data sharing; and (6) Evaluation in collaboration with the cities of Chicago and Evanston.

This award provides support for U.S-based students to attend the 25th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems, November 7-10, 2017 in Redondo Beach, CA (http://sigspatial2017.sigspatial.org/). In addition to regular research/demo papers and industry papers, ACM SIGSPATIAL 2017 features several sessions dedicated to young researchers: PhD Symposium, SIGSPATIAL Cup, and Student Research Competition. PhD Symposia in the past four years have also organized panels and keynote talks by prominent researchers, to help young researchers in improving the focus of their work. Spatial information is essential in many branches of industry and academia, and businesses as well as governmental agencies are utilizing it their daily operations to improve the structuring of new strategies, and increase overall productivity. Applications of spatial information handling can be found in location-based services in the mobile-commerce industry, strategic assessments in the military agencies, climatology studies (e.g., determining the potential effects of a tsunami) in meteorological research, computer-aided design and computer aided manufacturing (CAD/CAM) applications, medical imaging and atlases (e.g., 3D brain atlas), land-use classification of satellite imagery in urban planning, detection of local instability in transportation networks, epidemiological pattern forecasting (predicting the spread of disease) in the health-care field, analyzing crime hot spots for law enforcement, creation of high resolution three-dimensional maps from satellite imagery for intelligence applications, etc. Combined with the increased demand for spatial data processing, spatial information systems are gaining a substantial share across the spectrum in the industrial, government, and academic sectors. The ACM Special Interest Group on Spatial Information (SIGSPATIAL) is an association for researchers, students, developers, practitioners, and professionals interested in research, development, and deployment of solutions to spatial data, spatial operations, spatial information handling, and spatial knowledge extraction problems. The scope of members' interests spans from more theoretical aspects (e.g., geometric algorithms) to very focused application-oriented aspects, and has generated a significant body of research in the past decade. The charter of ACM SIGSPATIAL is dedicated to fostering research and development activities especially as it pertains to the acquisition, management, and processing of spatially-related information with a focus on algorithmic, geometric, and visual considerations, and is at the heart of enabling technologies for geographic information systems (GIS), navigation systems, etc. Given the relative importance of the field of spatial and geographical data, ACM SIGSPATIAL conference aims to augmenting its role and impact by motivating and supporting a new generation of scientists and engineers to take on the challenge of pursuing their graduate careers and beyond in this multidisciplinary area.

The ACM SIGSPATIAL conference has established itself as the world's premier research conference in Spatial Information and Geographic Information Systems (GIS). The conference provides a forum for original research contributions covering all conceptual, design, and implementation aspects of GIS ranging from applications, user interfaces, and visualization to storage management and indexing issues. It brings together researchers, developers, users, and practitioners carrying out research and development in novel systems based on geospatial data and knowledge, and fostering interdisciplinary discussions and research in all aspects of GIS. Proceedings of the ACM SIGSPATIAL 2017 Conference will be published in the ACM Digital Library (http://dl.acm.org/).