Collaborative Research: Modeling Networks with Multiple Physical Layers-The Case for Multi-Radio Networks

Northeastern University, Boston MA

Investigators

Abstract

Abstract 0634847 Collaborative Research PIs: Andras Farago, The University of Texas at Dallas Stefano Basagni, Northeastern University Modeling Networks with Multiple Physical Layers - The Case for Multi-Radio Networks Two major and lasting trends in the networking landscape are the growing importance of wireless networks and the increasing diversity of wireless networking solutions and standards. These trends come hand in hand with the rapidly decreasing cost and shrinking physical size of radio interfaces. It is now technically and economically feasible to put several radio transmitters/receivers in a single wireless network node. This creates an environment where the network effectively has multiple physical layers. This is expected to become ubiquitous in the future. While the technical possibility of multiple physical layers is already quite clear today, it is much less obvious how can this opportunity be efficiently utilized to gain significant improvement in the network performance. Or, from the practical/economical point of view, the ultimate question is: will the multiple physical layer (multi-radio) network development lead to sufficient performance improvement that justifies the investment? In this research project the investigators develop and analyze novel mathematical methods that can can quantify the network performance gain that is obtained via multiple physical layers. Specifically, the investigators model the network topology with an edge-labeled multigraph. This model offers surprisingly richer opportunities than the traditional graph model. Using this approach, the investigators study the following main areas: (1) quantifying the multi-radio gain in the network topology; (2) new algorithmic problems at the network layer; (3) new issues in network reliability; and (4) modeling and choosing routes in a mobile environment (5) experimental validation of the results via a testbed built in the project.