Subject: Mobility Models for Ad-Hoc Networks

From: "Justin's Dad" <[email-masked]>
Date: Thu, 15 Jul 1999 12:12:37 -0400 (EDT)

Hi,

I think that implementing precise individual node mobility 
models in a simulation is important in order to evaluate 
and conduct sensitivity analysis of ad-hoc routing 
performance. However, the question I would like to raise 
is as follows: 

Given that there are a number of good models that describe,
at least qualitatively, how we think mobile ad-hoc nodes 
might move under different scenarios, can we now develop 
analytical models, which characterize the dynamic behavior 
of the network topology? Specifically, given two nodes 
moving according to (for example) the Random Gauss-Markov 
model proposed by Haas and Liang, and reasonable 
assumptions governing signal propagation, is there an 
analytical expression that characterizes the distribution 
of the time-to-failure for a link between them?  Analytical 
expressions of this type could be very useful---for example 
in routing, or resource management schemes for ad-hoc 
networks.  

In cell networks the same thing is used to determine system 
performance measures, e.g. the distribution of the cell 
residence time is derived and used to evaluate various 
blocking probabilities, handover times etc.. These are
used in system design, also to dynamically allocate channels,
and in the future to provide support for probabilistic QoS 
to mobile users. In fact, many of these models have been based 
on random/brownian type movement.  To solve the same problem
in an ad-hoc network introduces a problem---we do not have 
the benefit of the fixed base station. Therefore, I think the 
problem is very difficult in general. The problem is even
mode difficult if you consider the possibility of
group movement.

Is there anyone who is attempting to solve this or any
similar problems? Does anyone have any insights or feeling
relative to value of developing such models? We have developed 
an analytical model for link availability based on a mobility 
model that is closely related to the Brownian motion model. 
Despite the apparent limitations of a brownian type model, 
it can be an effective model for aggregate node movement in a 
very large network. 

--Bruce McDonald

University of Pittsburgh