Modeling topology dissemination for routing in future force networks

The network-centric Future Force must support a large and diverse group of communication nodes. While much network design in networks such as Future Combat Systems (FCS) and Warfighter Information Network-Tactical (WIN-T) can be done using existing TCP/IP protocols, there are important performance limits when the network conditions become more extreme (e.g., highly mobile or dense). A solution for a particular environment requires performance approximation over a wide range of conditions and detailed simulation. This paper provides models and analysis of the expected user performance of different approaches to flooding link state routing information in wireless ad hoc networks for routing update. We compare the use of Flat Flooding, Multi-Point Relays (MPRs) and Connected Dominating Sets (CDSs), assuming other parts of the routing protocol are taken from standard Link State Routing protocol (e.g. OLSR) or Open Shortest Path First protocol (OSPF), widely used in the Internet and WIN-T to disseminate routing updates. In particular, we investigate what conditions will force the flat link state routing update mechanisms in OSPF to be augmented by more scalable solutions, such as OLSR MPRs and OSPF-MANET CDSs. Although the existing literature provides a variety of models for Flat Flooding and MPRs, it lacks similar analytical work for relays placement under the CDS approach. The only results available are obtained by simulation. In an attempt to compare all approaches under a similar analytical framework we focused on creating analytic models for CDS relay placements and deriving closed analytical formulae For this paper, we select one such model - the Hexagon - to represent the CDS approach in the comparative analysis, as it provides the lowest routing overhead among other properties. We also briefly introduce our heuristic that simulates the latter model as close as possible. Analysis shows the difference in the impact of conditions on key performance metrics. A key understanding of the models