Mobility prediction is defined as guessing the next access point(s) a mobile
terminal will join so as to connect to a (wired or wireless) network. Knowing
in advance where a terminal is heading for allows taking proactive measures so
as to mitigate the impact of handovers and, hence, improve the network QoS.
This thesis analyzes this topic from different points of view. It is divided
into three parts.
The first part evaluates the feasibility of mobility prediction in a real
environment. It thus analyzes a mobility trace captured from a real network to
measure the intrinsic entropy of the nodes motion and to measure the
effectiveness of a simple prediction method.
The second part investigates how to perform mobility prediction. Firstly,
it examines a generic prediction scheme based on a simple machine learning
method; this scheme is evaluated under various conditions. Secondly, it shows
how the pieces of information that are most useful for the prediction algorithm
can be obtained.
The third part studies how knowing the probable next access point of a mobile
terminal allows one to improve the QoS of the network considered. We deal
with two situations. We ﬁrst show how the handover blocking rate
of a cellular network can be decreased thanks to resource reservation. We
then propose a new routing protocol for delay tolerant networks (i.e. an ad
hoc network where packets must be delayed in the absence of an end-to-end path)
that assumes that the contacts between the nodes can be (imperfectly) predicted.