  Towards realizing an intelligent networked society, enabling low-cost
low-energy connectivity for things, also known as Internet of Things (IoT), is
of crucial importance. While the existing wireless access networks require
centralized signaling for managing network resources, this approach is of less
interest for future generations of wireless networks due to the energy
consumption in such signaling and the expected increase in the number of IoT
devices. Then, in this work we investigate leveraging machine learning for
distributed control of IoT communications. Towards this end, first we
investigate low-complex learning schemes which are applicable to
resource-constrained IoT communications. Then, we propose a lightweight
learning scheme which enables the IoT devices to adapt their communication
parameters to the environment. Further, we investigate analytical expressions
presenting performance of a centralized control scheme for adapting
communication parameters of IoT devices, and compare the results with the
results from the proposed distributed learning approach. The simulation results
confirm that the reliability and energy efficiency of IoT communications could
be significantly improved by leveraging the proposed learning approach.
