The performance of any mobile network is highly influenced by the mobility patterns of nodes in the network. In fact mobility behavior is the most important environmental factor that determines performance and influences network design. Thus it is important that we develop models that (1) accurately capture the mobility of nodes, while at the same time (2) are amenable to mathematical analysis and/or efficient simulation. In the past, the wireless networking community relied on simple models such as random walk [3] and random way- point [2]. However, the first of these models was found to be too simplistic although very useful in analysis and simulation. The latter, on the other hand was widely accepted and used in simulations. However, recently it was discovered that practitioners had severely misused it in simulations [4]. That and the growing appreciation that random waypoint is unrealistic has led to significant activities recently focusing on the development of parsimonious and accurate models describing the movement of nodes in mobile wireless networks. These have included efforts to model connectivity within sparse mobile networks associated with people and vehicles, and with the movement of units in miliatary settings. This talk will provide some of the early history in modeling mobility, including some of the failures described above. The talk will then describe the more recent application of a wide range of techniques to account for mobility. These range from Markov chains to ODE-based fluid models [6], from the use of exponential, lognormal, and heavy- tailed distributions [l]to geometric weighted Gaussian distributions for intercontact times [5], and from the use of discrete state models to continuous state PDE-based models. The goal of the talk is to provide a better understanding of the issues underlying the accurate modeling of mobility, recent progress in addressing these issues and the challenges that different approaches place on simulation.