  The Internet of Things (IoT) is an emerging technology that aims to connect
heterogeneous and constrained objects to each other and to the Internet. It has
grown significantly in a wide variety of applications such as smart homes,
smart cities, smart vehicles, etc. The huge number of connected devices
increases the challenges, as IoT provides diverse and complex network services
with different requirements on a common infrastructure. Network Softwarization
is the latest network paradigm that transforms traditional network processes to
the separation of hardware and software by using some enabling network
technologies such as Software Defined Networking (SDN) and Network Function
Virtualization (NFV). Machine Learning (ML) plays an essential role in creating
smarter IoT networks, as it has shown remarkable results in various domains.
Given that the network softwarization allows it to be easily integrated, ML can
play a crucial role in efficient and self-adaptive IoT networks. In this paper,
we provide a detailed overview of the concepts of IoT, network softwarization,
and ML, and we study and discuss the state of the art of intelligent ML-enabled
network softwarization for IoT. We also identify the most prominent future
research directions to be considered.
