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Publication Detail
An ad hoc mobility model founded on social network theory
  • Publication Type:
  • Authors:
    Musolesi M, Hailes S, Mascolo C
  • Publisher:
    ACM Press
  • Publication date:
  • Place of publication:
    New York, NY, USA
  • Pagination:
    20, 24
  • ISBN-10:
  • Book title:
    Proceedings of the 7th ACM/IEEE International Symposium on Modeling, Analysis and Simulation of Wireless and Mobile Systems, Venice, Italy
  • Notes:
    Imported via OAI, 7:29:01 26th Jul 2005
Almost all work on mobile ad hoc networks relies on simulations, which, in turn, rely on realistic movement models for their credibility. Since there is a total absence of realistic data in the public domain, synthetic models for movement pattern generation must be used and the most widely used models are currently very simplistic, the focus being ease of implementation rather than soundness of foundation. Whilst it would be preferable to have models that better reflect the movement of real users, it is currently impossible to validate any movement model against real data. However, it is lazy to conclude from this that all models are equally likely to be invalid so any will do.We note that movement is strongly affected by the needs of humans to socialise in one form or another. Fortunately, humans are known to associate in particular ways that can be mathematically modelled, and that are likely to bias their movement patterns. Thus, we propose a new mobility model that is founded on social network theory, because this has empirically been shown to be useful as a means of describing human relationships. In particular, the model allows collections of hosts to be grouped together in a way that is based on social relationships among the individuals. This grouping is only then mapped to a topographical space, with topography biased by the strength of social tie.We discuss the implementation of this mobility model and we evaluate emergent properties of the generated networks. In particular, we show that grouping mechanism strongly influences the probability distribution of the average degree (i.e., the average number of neighbours of a host) in the simulated network.
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