Information Science & Technology Division
The Collaboratory performs wide ranging fundamental research on Sensor Networks & their Applications, Analytical Modeling & Analyis of Complex Dynamic Systems, Intelligent Control of Autonomous Systems, Discrete Event Systems, Distributed Information Systems and Cyber Security .

The Information Science and Technology (IST) Division conducts nationally competitive multi-disciplinary innovative research in information science and technology for conceptualization, design, analysis, deployment, and efficient operation of distributed and intelligent dynamic systems for the DoD, Government, business, and education sectors.

Established in 1997, IST was initially formed to establish the National Information Infrastructure Testbed (NII), a ten year university/industry partnership funded by DARPA. The NII Testbed developed advanced sensor nework and information sharing tools for prognosis and mitigation of material damage in operating machinery.

The current IS&T Division is made up of three distinct departments:

• Analytical Modeling
  
  Information fusion is defined as process in which information from disparate sources are efficiently combined into a single overarching source thus providing more information about an event that would not otherwise be possible using individual sources. The Sensors and Control Laboratory provides supports to Information Fusion research by providing advanced networks of wired and wireless heterogeneous sensors. Current application of interest in Information Fusion includes associating video surveillance data with biological, chemical, infrared, acoustic, and positional data for TTL (tagging, tracking, and locating) of human subjects in a busy urban environment. The idea is to associate chemical, biological, and biometric signals emanating from a target to the corresponding video data stream by utilizing computational mechanics and symbolic dynamic methods. Computational mechanics employs ε-machines (probabilistic finite state machines) to determine underlying patterns in data streams. By utilizing ε-transducer (probabilistic finite state transducers), the underlying pattern in one sensor data stream can be associated to patterns in other sensor data streams.

• Intelligent Control

          The Intelligent Controls Department 

• Distributed Systems
  
  The Distributed Systems Department conducts advanced research in the design and applications of real and non-real time systems that have
  multiple and spatially distributed resources that communicate over scalable and multi-technology networks. Our primary focus is on
  systems that use autonomous mobile and stationary platforms that have heterogeneous sensors and actuator to support military, industrial and civilian applications that require persistent monitoring and control of the operating environments.
  Our research also spans utilizing contemporary distributed computing technologies such as the SoA, Semantic Web and the Ontology of
  distributed autonomous systems. Since a distributed autonomous systems is subject to failures due to software, hardware and external cyber attack,the group also conducts research in survivability modeling and cyber security.

The IST Division maintains state-of-the-art testbeds and computing frameworks for prototyping or simulating innovations in wireless mobile robotic networks, sensors and controls, and augmented reality smart spaces.

• Information Systems Collaboratory provides the distributed infrastructure for laboratories and testbeds focused on research of dynamic control and coordination of complex interacting processes and distributed decision automata with real-time and/or transient information.
• The Mobile Robotics Laboratory emphasizes robotic networking, logical control and interactive behavior to design, implement and test mobile robotics systems for real world applications.
• The Sensors and Control Laboratory supports advanced research in sensor fusion, wireless sensor networks, distributed control theory, hierarchical control systems, and hybrid control architectures.
• The Augmented Reality Smart Space Laboratory, using sensing, actuation, and distributed processing, brings reality into cyberspace enabling human computer interaction, cybersecurity, environmental monitoring, mobile code, and detection of airborne pathogens.
• The Smart Urban Sensors Network Laboratory, using live and virtual sensors collaboratively disseminating their data over wired and wireless networks, provides a testbed for research in network design and control to support improved dynamic sensor fusion for enhanced detection and tracking of critical events in the complex and volatile urban environment.

Some of our projects included:
Engineering Sensor Network Structure for Information Fusion (eSensIF) MURI and Complex Systems Failures MURI (funded by ARO),
Reactive Sensor Networks (RSN), Adaptive C2 Coalitions (AC2C), and Emergent Surveillance Plexus (ESP) MURI  (funded by DARPA),
Geoinformatic Hotspot Analysis (funded by NSF), Dynamic Space-time Clustering of Multi-source Assets For Early Localization of Mines project, Mobile Ubiquitous Security Environment (MUSE) , and Ocean Sampling Mobile Network (SAMON)  (funded by ONR).

Additional Information: