Autonomic Networked Information and Control Systems: 
The Critical Effects of Connectivity Architectures

John S. Baras
Institute for Systems Research and 
Department of Electrical and Computer Engineering and
Department of Computer Science
Fischell Department of Bioengineering
University of Maryland College Park
USA

Abstract
We develop a unifying analytical and optimization framework for networks of autonomous agents. The fundamental view is that agents in such a network are dynamic entities that collaborate because via collaboration they can accomplish objectives and goals much better than working alone, or even accomplish objectives that they cannot achieve alone at all. Yet the benefits derived from such collaboration require some costs (or expenditures), for example due to communications. Or in equivalent terms, the collaboration is subject to constraints (static and dynamic). Understanding and quantifying this tradeoff between the benefits vs the costs of collaboration, leads to new methods that can be used to analyze, design and control/operate networks of agents. An important, yet not emphasized todate, aspect of collaboration is the role of topologies and connectivities linking the agents. This is the main topic of this paper. We demonstrate that connectivities related to small world graphs and expander graphs offer dramatic advantages with respect to many problems that need to be solved in a distributed manner in collaborative information and control systems. These topologies are efficient from the perspective of costs yet lead to fast convergence of distributed algorithms from the benefits perspective. We show how they can be self-generated in response to cost-benefit tradeoffs. We illustrate by simple examples from communication, social and biological networks.