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Abstract|Graphs are commonly used to model the topological structure of internetworks, to study problems ranging from routing to resource reservation. A variety of graphs are found in the literature, including xed topologies such as rings or stars, well-known" topologies such as the ARPAnet, and randomly generated topologies. While many researchers rely upon graphs for analytic and simulation studies, there has been little analysis of the implications of using a particular model, or how the graph generation method may a ect the results of such studies. Further, the selection of one generation method over another is often arbitrary, since the di erences and similarities between methods are not well understood.This paper considers the problem of generating and selecting graph models that re ect the properties of real internetworks. We review generation methods in common use, and also propose several new methods. We consider a set of metrics that characterize the graphs produced by a method, and we quantify similarities and di erences amongst several generation methods with respect to these metrics. We also consider the e ect of the graph model in the context of a speci c problem, namely multicast routing.

Abstract-Disruption Tolerant Networks (DTNs) are designed to overcome limitations in connectivity due to conditions such as mobility, poor infrastructure, and short range radios. DTNs rely on the inherent mobility in the network to deliver packets around frequent and extended network partitions using a store-carry-andforward paradigm. However, missed contact opportunities decrease throughput and increase delay in the network. We propose the use of throwboxes in mobile DTNs to create a greater number of contact opportunities, consequently improving the performance of the network. Throwboxes are wireless nodes that act as relays, creating additional contact opportunities in the DTN. We propose algorithms to deploy stationary throwboxes in the network that simultaneously consider routing as well as placement. We also present placement algorithms that use more limited knowledge about the network structure. We perform an extensive evaluation of our algorithms by varying both the underlying routing and mobility models. Our results suggest several findings to guide the design and operation of throwbox-augmented DTNs.