  Internet-of-Things (IoT) refers to a massively heterogeneous network formed
through smart devices connected to the Internet. In the wake of disruptive IoT
with a huge amount and variety of data, Machine Learning (ML) and Deep Learning
(DL) mechanisms will play a pivotal role to bring intelligence to the IoT
networks. Among other aspects, ML and DL can play an essential role in
addressing the challenges of resource management in large-scale IoT networks.
In this article, we conduct a systematic and in-depth survey of the ML- and
DL-based resource management mechanisms in cellular wireless and IoT networks.
We start with the challenges of resource management in cellular IoT and
low-power IoT networks, review the traditional resource management mechanisms
for IoT networks, and motivate the use of ML and DL techniques for resource
management in these networks. Then, we provide a comprehensive survey of the
existing ML- and DL-based resource allocation techniques in wireless IoT
networks and also techniques specifically designed for HetNets, MIMO and D2D
communications, and NOMA networks. To this end, we also identify the future
research directions in using ML and DL for resource allocation and management
in IoT networks.
