EAGER: Distributed Network Optimization and Consensus with Event-Driven Communications

University Of Maryland, College Park, College Park MD

Investigators

Abstract

The objective of this research is to develop techniques to allow multiple decision makers to jointly solve optimization and learning problems through individual computations and with limited communications among the decision makers contributing to an area known as distributed optimization and learning. Here, each decision maker is viewed as an intelligent agent, modeled as a node in a network of agents. Communication between the agents can occur only if their nodes are connected by a link of the network graph. This framework allows modeling of large systems consisting of multiple agents where each agent collects data from the environment as well as from its neighboring agents. The agents process the received data and communicate with one another over time. This research project aims to extend existing knowledge by reducing the requirement that nodes must communicate acknowledgments from neighboring nodes and reducing the amount of communication by using event-driven communications, where communication between neighboring nodes is triggered by the occurrence of pre-defined events. Applications considered include networks of unmanned aerial vehicles and electric power grids. The project will provide an impact on students at the university and through outreach to local high schools. The research project will pose novel problems for distributed networks of communicating agents tasked with achieving goals defined globally while using limited communication. The project also aims to define new applications-motivated distributed network problems, to make theoretical headway on these problems, and to pursue several applications of the theory in detail. A main goal of this work is to extend existing algorithms to the event-triggered framework. These events can be defined in terms of local system variables meeting certain conditions, or as arrival events where certain types of information are received from the external environment. The research project will model both types of events as well as their influence on the dynamics of distributed decision making. For exogenous arriving information, the researchers will consider occasional arrivals of exogenous data at a smaller set of specialized nodes. Stability of the designed distributed algorithms will be analyzed relative to the arrival processes, the network dynamics, and the graph structure. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.