Routing Optimization in Interplanetary Networks

Authors

Sara El Alaoui, University of Nebraska-LincolnFollow

Date of this Version

Fall 12-2015

Citation

S. El Alaoui, "Routing Optimization in Interplanetary Networks," Master's Thesis, University of Nebraska-Lincoln, December 2015.

Comments

A THESIS Presented to the Faculty of The Graduate College at the University of Nebraska In Partial Fulfillment of Requirements For the Degree of Master of Science, Major: Computer Science, Under the Supervision of Professor Byrav Ramamurthy. Lincoln, Nebraska: December, 2015

Copyright (c) 2015 Sara El Alaoui

Abstract

Interplanetary Internet or Interplanetary Networking (IPN) is envisaged as a space network which interconnects spacecrafts, satellites, rovers and orbiters of different planets and comets for efficient exchange of scientific data such as telemetry and images. IPNs are classified among challenged networks because of the unpredictable changes in the network and the large varying delays in communication. These networks are hard to model using static graphs and do not behave optimally when operated using the standards and techniques of static networks. Delay Tolerant Networking (DTN), in its different implementations, is one of the suggested solutions to overcome these challenges. DTN has different routing techniques, among which Contact Graph Routing (CGR) is the more widely used in IPNs. In this thesis, we identify the shortcoming of CGR that results from overlooking the future contacts, and we propose the Earliest Arrival Optimal Delivery Ratio (EAODR) Routing that examines all the paths both with the desired earliest departure time and in the future in order to choose the earliest arrival path from a given node. EAODR finds the route that delivers the exchanged message (a. k. a. bundle) at most at the same time as CGR's route. In order to do that, we propose a Modified Temporal Graph (MTG) model that provides a near-real-time representation of the deterministic dynamic networks. We base EAODR routing algorithm on the MTG model. Our results show that we can reduce the delay by 12.9% compared to CGR when we apply our algorithm to over 50 combinations of bundle sizes and transmission times.

Adviser: Byrav Ramamurthy