  Intelligent network selection plays an important role in achieving an
effective data offloading in the integrated cellular and Wi-Fi networks.
However, previously proposed network selection schemes mainly focused on
offloading as much data traffic to Wi-Fi as possible, without systematically
considering the Wi-Fi network congestion and the ping-pong effect, both of
which may lead to a poor overall user quality of experience. Thus, in this
paper, we study a more practical network selection problem by considering both
the impacts of the network congestion and switching penalties. More
specifically, we formulate the users' interactions as a Bayesian network
selection game (NSG) under the incomplete information of the users' mobilities.
We prove that it is a Bayesian potential game and show the existence of a pure
Bayesian Nash equilibrium that can be easily reached. We then propose a
distributed network selection (DNS) algorithm based on the network congestion
statistics obtained from the operator. Furthermore, we show that computing the
optimal centralized network allocation is an NP-hard problem, which further
justifies our distributed approach. Simulation results show that the DNS
algorithm achieves the highest user utility and a good fairness among users, as
compared with the on-the-spot offloading and cellular-only benchmark schemes.
