  Nowadays, most mobile devices are equipped with multiple wireless interfaces,
causing an emerging research interest in device to device (D2D) communication:
the idea behind the D2D paradigm is to exploit the proper interface to directly
communicate with another user, without traversing any network infrastructure. A
first issue related to this paradigm consists in the need for a coordinator,
called controller, able to decide when activating a D2D connection is
appropriate and eventually able to manage such connection. In this view, the
paradigm of Software Defined Networking (SDN), can be exploited both to handle
the data flows among the devices and to interact directly with every device.
  This work is focused on a scenario where a device is selected by the SDN
controller, in order to become the master node of a WiFi-Direct network. The
remaining nodes, called clients, can exchange data with other nodes through the
master. The objective is to infer, through different Machine Learning
approaches, the number of nodes actively involved in receiving data, exploiting
only the information available at the client side and without modifying any
standard communication protocol. The information about the number of client
nodes is crucial when, e.g., a user desires a precise prediction of the
transmission estimated time of arrival (ETA) while downloading a file.
