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Internet-of-Things (IoT) refers to a massively heterogeneous network formedthrough smart devices connected to the Internet. In the wake of disruptive IoTwith 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 IoTnetworks. Among other aspects, ML and DL can play an essential role inaddressing the challenges of resource management in large-scale IoT networks.In this article, we conduct a systematic and in-depth survey of the ML- andDL-based resource management mechanisms in cellular wireless and IoT networks.We start with the challenges of resource management in cellular IoT andlow-power IoT networks, review the traditional resource management mechanismsfor IoT networks, and motivate the use of ML and DL techniques for resourcemanagement in these networks. Then, we provide a comprehensive survey of theexisting ML- and DL-based resource allocation techniques in wireless IoTnetworks and also techniques specifically designed for HetNets, MIMO and D2Dcommunications, and NOMA networks. To this end, we also identify the futureresearch directions in using ML and DL for resource allocation and managementin IoT networks.

Machine Learning for Resource Management in Cellular and IoT Networks: Potentials, Current Solutions, and Open Challenges