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Network Sciences

U.S. Army Research Office
ATTN: RDRL-ROI-N
P.O. Box 12211
Research Triangle Park, NC 27709-2211

Commercial: (919) 549-4321
DSN: 832-4321
Fax: (919) 549-4310

Work over the past 10 years by researchers in various fields including Statistical Mechanics, Anthropology, Structural Biology, Distributed Systems, Theoretical Computer Science, Robotics and Control theory has shown that there is a lot of commonality in the structure of networks around us be it communication among a school of fish, pack of wolves, a group of jihadis, or an adhoc wireless network. The goal of the Network Sciences program is to make use of this commonality, in a synergistic way, to address issues of importance to the Army. Networks of sensors, communication and computation nodes, and robots are pervasive throughout the Army and especially in Command, Control, Communications, Computing, Intelligence, Surveillance, and Reconnaissance (C4ISR) systems. The Network Science program identifies and addresses the Army's critical basic research problems in C4ISR where progress has been inhibited by a lack of novel concepts or fundamental knowledge. Research in this program has application to a wide variety of developmental efforts and contributes to the solution of technology-related problems throughout the Army's Future Force operational goals. The Network Sciences program is divided into the following areas of research:

• Communications and Human Networks addresses research for the fundamental understanding of wireless communications and human networks. In communications, the programs focuses on research to further the understanding of tactical mobile wireless communication for the battlefield of the future. For human networks, identifying structure of networks from diverse data is of particular interest.
• Intelligent Networks augment human decision makers with enhanced-embedded battlefield intelligence that will provide them with tools for creating necessary situational awareness, reconnaissance, and decision making to decisively defeat any future adversarial threats. The challenge is to find methods that facilitate the development of intelligent and autonomous systems that perceive their environment by means of sensing and through context, and use that information to generate intelligent, goal-directed desired behaviors.
• Multi-Agent Network Control is concerned with modeling, analysis, design, and robust control of complex real-time dynamic systems, including distributed and embedded, networked autonomous and semi-autonomous, non-linear, embedded and hybrid, and decentralized systems. The program also involves innovative research on emerging areas such as net-centric control and the interaction of control with biological organisms.
• Decision and Neuro Sciences addresses development of new advanced modeling, simulation, optimization and other analysis methodologies to support command-level decision-making at the operational level.

The Network Sciences Division supports the following research areas:

Division Chief

Dr. Purush Iyer
(919) 549-4204
purush.iyer@us.army.mil

Communications & Human Networks

Dr. Robert Ulman
(919) 549-4330
robert.ulman@us.army.mil

Research in this area is concerned with the application of communications and network theory, signal processing, and mathematics to enable the fast, accurate, reliable, and efficient transmission of information for the wireless digital battlefield. Due to their low probability of interception, anti-jam, and multiple access characteristics, spread spectrum techniques are important to Army communications, intelligence, surveillance, and target acquisition systems. Methods for design and performance analysis of spread spectrum systems are being studied. The vulnerability of spread spectrum systems to jamming and interference and the use of adaptive electronic counter countermeasures (ECCM) techniques to improve network performance in the presence of jamming and interference are being investigated. Network science is being investigated to understand the fundamental limits of wireless ad hoc networks and the performance of proposed algorithms.

The digital battlefield requires a seamless, ubiquitous, survivable and highly mobile wireless communication system with a highly dynamic network topology. The information communicated ranges from voice to video and includes bursty file transfers for vehicle and aircraft radio, as well as light weight radios carried by Soldiers on foot. The channels are noisy and unreliable due to jamming, mobility, multipath, and multi-user interference. To provide the necessary capability, research is supported in spread spectrum, mobile ad hoc radio networks in the areas of multimedia network architectures, distributed routing, congestion control, and heterogeneous network integration. Research is also supported in adaptive source and channel coding, networking with adaptive antennas, adaptive routing to avoid failed nodes, and power control. Of particular interest is the science of networks as applied to the tactical wireless network problem, including an understanding of its performance limits. Finally, of growing interest is the use of the concepts used in cognitive radio applied to the overall network in the emerging area of cognitive networks.

Decision & Neuro-Sciences

Dr. Janet Spoonamore
919.549.4284
janet.spoonamore@us.army.mil

The objective of the Decision and Neuro-Sciences program is to develop theoretical foundations, models, and algorithms to support timely, robust, near-optimal decision making in highly complex, dynamic systems, operating in uncertain, resource-constrained environments with incomplete information against a competent thinking adversary. Although, based on operations research methodologies such as modeling, simulation and numerical optimization, this program is expected to include multi-disciplinary emphasis to accommodate complex, multi-dimensional decision frameworks in today's asymmetric warfare. Examples of research that could contribute towards this unifying goal are:

• Modeling and simulation of contemporary environments (addressing adversarial strategies, classic terrain features, demographically-informed population information, as well as dynamic temporal information) with the objective of decision support.
• Fundamental graph theory and network analysis in support of modeling social networks and other complex systems behaviors and processes.
• Numerical optimization and modeling to include capabilities for stochastic behaviors; novel approaches which address more general conditions and distributions.
• Bayesian and other evidential reasoning and fusion approaches to model wide ranging, perhaps real-time, and incomplete information.
• Sequential dynamic decision making approaches.
• New algorithms with provable or demonstrable improved performance bounds.
• Game theoretical and simulation approaches applied to asymmetric warfare situations.
• Empirical studies into physiological, psychological and cognitive modeling of decision making.

This new program. has included work in developing design-driven validation and verification methods developed using Gaussian stochastic process to estimate measurement error and construct prediction for a weapons systems life-cycle simulation model.

Another project is developing solutions of new classes of stochastic differential equations to assess network survivability and uses Bayesian tracking to quantify trust within a social network. Further, coupled systems of nonlinear stochastic differential equations are developed to model network dynamics. A recent project on numerical optimization has integrated and expanded knowledge from the pattern recognition into stochastic programming techniques, providing a modeling framework for stochastic constraints using combinatorial patterns. Of note, another new work develops an empirically supported model of strategic human judgment by investigating and modeling human subjects' probability assessments in settings of simulated unmanned aerial vehicles. In this work, specific studies investigate the nature of honesty in reporting true probability assessment and the impact of inaccuracies due to verbal expressions of probabilities.

Intelligent Networks

Dr. Purush Iyer
(919) 549-4204
purush.iyer@us.army.mil

The objective of this task is to augment human decision makers (both commanders and Soldiers) with enhanced-embedded battlefield intelligence that will provide them with the necessary situational awareness, reconnaissance, and decision making tools to decisively defeat any future adversarial threats. The challenge is to find methods that facilitate the development of intelligent and autonomous systems that perceive their environment by means of sensing and through context, and use that information to generate intelligent, goal-directed, desired behaviors. This area of research poses unique challenges for the Army as it involves developing autonomous capability for mixed teams of air and ground vehicles that acts to complement a Soldier's capabilities.

The focus is on developing a formalized mathematical, algorithmic, and practical understanding of perception, control and learning to facilitate the development of intelligent and autonomous systems. This approach requires research in the following areas:

• Integrated Intelligence, where sub-components for vision, knowledge representation, reasoning, and planning are integrated in a synergistic fashion to yield a sum that is more than its parts.
• Robust Reasoning Under Uncertainty, where the ability to adapt or compensate, in reasoning, for the uncertainty inherent in real systems related to modeling error, sensing errors and noise, system failures, and changing dynamic environments, are important.
• Socio-Cultural Modeling/Computing, which brings together elements of Game Theory, Knowledge representation and Social sciences to reason about groups/societies.

Multi-Agent Network Control

The Multi-Agent Network Control program is concerned with developing the theory and tools, through appropriate application and creation of the relevant mathematics, to the modeling, analysis, design, and robust control of complex real-time physical and information-based systems; including distributed and embedded, networked autonomous and semi-autonomous, non-linear, smart structures, and decentralized systems. The program invests in fundamental systems and control theory and relevant mathematical foundations for areas of control science such as multi-variable control, non-linear control, stochastic and probabilistic control distributed and embedded control, and multi-agent control theory. Further, the program also involves innovative research on emerging areas such as control of complex systems and theories for the design of large heterogeneous multi-agent teams with desired emergent behaviors.

• Control Theory and Related Mathematics - Topics of interest include multivariable control for robust performance in the presence of measurement and model uncertainties, including adaptive, nonlinear, optimal, stochastic, embedded and hybrid control, learning systems, swarming behaviors, game theory, and decision-making. Additional areas of interest are in distributed multi-agent theory with applications to heterogeneous teams of robotic, UAVs, biological entities, and/or software.
• Net-Centric, Distributed, Autonomous and Semi-Autonomous Systems - The anticipated dynamics of the future battle space will require a greatly increased level of automation to enable the necessary mobility, sensor coverage, information flow, and responsiveness to support the military goals of information superiority, dominant maneuver, and precision engagement. Intelligent collaborative networks of software and physical agents will allow the Army to satisfy this increased tempo within the constraints of reduced manpower and casualties. Topics of interest include integrated agent-based decision and control architectures, dynamic resource management, and fault-tolerant operation, especially under bandwidth communication and computational constraints. Further, the program is interested in extending mathematical foundations related to distributed system theory; metrics for system complexity, information content, flow, structure, swarming phenomena, design of emergent behavior for heterogeneous multi-agent systems, and information processing and data fusion for decision-making.

Additional Information

• Research programs from ARL Broad Agency Announcement
• Research listing from ARO-In-Review On-line publication