NeTS: Small: Collaborative Research: Large Scale Networks and Information Flow: From Emergent Behavior to Algorithm Design

Yale University, New Haven CT

Investigators

Abstract

This project studies the performance of complex large-scale spatial networks that use distributed communication algorithms by applying different random network models. While much of the previous work on random spatial networks has focused on topological aspects and on centralized algorithm design, the emphasis of this project is on developing distributed algorithms and on characterizing desired invariant network properties that such networks must satisfy to allow for the efficient implementation of distributed algorithms. Expected results from the project include (1) decentralized algorithms for efficient path navigation, (2) decentralized algorithms for information flow in random networks, (3) decentralized algorithms for information dissemination, (4) distributed energy management in random networks, and (5) distributed topology control for maintaining percolation-based connectivity. This project will (1) increase the understanding of complex information systems upon which society increasingly depends, (2) lead to design guidelines for practical network architectures and protocols, (3) disseminate the scientific and technological findings through publications, (4) further the development of related disciplines such as mathematics (probability) and statistical physics, (5) impact undergraduate and graduate education through a planned joint course on random networks, and a summer lecture series, (6) enhance research experiences for undergraduates through summer research projects, and (7) encourage the participation of women and minorities in networking research.