EAGER: Identification and Design of Optimal Communication Topologies in Collaborative Networks

Syracuse University, Syracuse NY

Investigators

Abstract

Large networks of distributed interacting agents are omnipresent in modern society and technological applications. Examples are wide-ranging and include sociopolitical networks, satellite and sensor constellations, vehicular formations, and the emerging smart grid. As networks increase in their size and complexity, it is imperative to develop the next generation of quantitative infrastructure to understand their collective behavior and to enhance their performance. The theory and techniques developed in this proposal are expected to serve as a foundation for designing secure, resilient, and efficient networks of the future. Application areas of the proposed work include information dissemination in social networks, distributed decision-making in multi-agent systems, and stability and efficiency improvement in distributed power generation networks. By engaging graduate students in research, the project will contribute to the workforce needs in cybersecurity and network science. The field of sparse signal recovery, also referred to as compressive sensing, has witnessed overwhelming research activity across different scientific communities over the past decade, accompanied by the development of theoretical and computational tools. In signal recovery it is of interest to expose sparse patterns hidden in under-sampled signals and large datasets. Problems of network identification and design however, while just as important, have not received as much attention. The focus of such problems is to uncover pivotal sparse structures that are responsible for the efficient and resilient functioning of large networks. The research objective of this proposal is to develop an analytic and computational framework for the analysis and design of collaborative networks. Models of dynamical networks are employed that lend themselves to topological variations and augmentation. Measures of uncertainty amplification are used to quantify nodes collective behavior across time and space. Resource-aware constraints and perturbation methods are employed in the context of convex optimization to identify pivotal communication and interaction topologies that balance the use of resources with high collective performance. As a concrete application, the proposed framework is used to find optimal communication topologies in networks of synchronizing agents.